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Sahiledengleetal.
Journal of Health, Population and Nutrition (2023) 42:7
https://doi.org/10.1186/s41043-023-00347-9
RESEARCH
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Open Access
Journal of Health, Population
and Nutrition
Association betweenmaternal stature
andhousehold-level double burden
ofmalnutrition: ndings fromacomprehensive
analysis ofEthiopian Demographic andHealth
Survey
Biniyam Sahiledengle1*, Lillian Mwanri2 and Kingsley Emwinyore Agho3,4,5
Abstract
Background Undernutrition among under-five children is one of the intractable public health problems in Ethiopia.
More recently, Ethiopia faced a rising problem of the double burden of malnutrition—where a mother may be over-
weight/obese, and a child is stated as having undernutrition (i.e., stunting, wasting, or underweight) under the same
roof. The burden of double burden of malnutrition (DBM) and its association with maternal height are not yet fully
understood in low-income countries including Ethiopia. The current analysis sought: (a) to determine the prevalence
of double burden of malnutrition (i.e., overweight/obese mother paired with her child having one form of undernutri-
tion) and (b) to examine the associations between the double burden of malnutrition and maternal height among
mother–child pairs in Ethiopia.
Methods We used population-representative cross-sectional pooled data from four rounds of the Ethiopia Demo-
graphic and Health Survey (EDHS), conducted between 2000 and 2016. In our analysis, we included children aged
0–59 months born to mothers aged 15–49 years. A total of 33,454 mother–child pairs from four waves of EDHS were
included in this study. The burden of DBM was the primary outcome, while the maternal stature was the exposure of
interest. Anthropometric data were collected from children and their mothers. Height-for-age (HFA), weight-for-height
(WFH), and weight-for-age (WFA) z-scores < − 2 SD were calculated and classified as stunted, wasting, and under-
weight, respectively. The association between the double burden of malnutrition and maternal stature was examined
using hierarchical multilevel modeling.
Results Overall, the prevalence of the double burden of malnutrition was 1.52% (95% CI 1.39–1.65). The prevalence
of overweight/obese mothers and stunted children was 1.31% (95% CI 1.19–1.44), for overweight/obese mothers and
wasted children, it was 0.23% (95% CI 0.18–0.28), and for overweight/obese mothers and underweight children, it was
0.58% (95% CI 0.51–0.66). Children whose mothers had tall stature (height ≥ 155.0 cm) were more likely to be in the
double burden of malnutrition dyads than children whose mothers’ height ranged from 145 to 155 cm (AOR: 1.37,
95% CI 1.04–1.80). Similarly, the odds of the double burden of malnutrition was 2.98 times higher for children whose
*Correspondence:
Biniyam Sahiledengle
biniyam.sahiledengle@gmail.com
Full list of author information is available at the end of the article
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Sahiledengleetal. Journal of Health, Population and Nutrition (2023) 42:7
mothers had short stature (height < 145.0 cm) (AOR: 2.98, 95% CI 1.52–5.86) compared to those whose mothers had
tall stature.
Conclusions The overall prevalence of double burden of malnutrition among mother–child pairs in Ethiopia was less
than 2%. Mothers with short stature were more likely to suffer from the double burden of malnutrition. As a result,
nutrition interventions targeting households’ level double burden of malnutrition should focus on mothers with short
stature to address the nutritional problem of mother and their children, which also has long-term and intergenera-
tional benefits.
Keywords Double burden of malnutrition, Dual forms of malnutrition, Ethiopia, Maternal stature, Mother–child pairs,
Overweight mothers, Underweight child
Introduction
e coexistence of overweight and undernutrition among
the members of a single household known as the double
burden of malnutrition (DBM) has drawn more atten-
tion in recent years [1]. Undernutrition and overnutrition
coexist simultaneously despite previously being under-
stood and treated as separate public health problems [2,
3]. According to the World Health Organization (WHO),
the DBM is “characterized by the coexistence of under-
nutrition along with overnutrition (overweight and obe-
sity), or diet-related non-communicable diseases, within
individuals, households and populations, and across the
life-course” [4]. At the household level, a double burden
of malnutrition can exist—when a mother may be over-
weight/obese and a child has an undernutrition status
(i.e., stunting, wasting, or underweight) [5].
Globally, around 45% of deaths among children under
5years are linked to undernutrition [6]. It is also esti-
mated that 149.2 million children under 5 suffered from
stunting, while wasting affected 49 million children
under the age of 5 in 2020 [6]. Evidence also showed that
two out of five and more than one-quarter of all children
suffering from stunting and wasting lived in Africa [6)].
Meanwhile, in sub-Saharan Africa (SSA), the prevalence
of overweight and obesity is rising rapidly, with adult
women bearing the greatest burdens—ranging from
5.6 to 27.7% [7]. A recent study examining the trends of
overweight and obesity among women in Africa showed
statistically significant increasing trends in several SSA
countries [8]. Furthermore, due to the rapid ongoing
global nutrition transition, an increasing number of stud-
ies demonstrate that the double burden of malnutrition
(DBM) is a particular challenge for low- and middle-
income countries (LMICs) [1, 9–17].
Although research is limited, sub-Saharan Africa has
also been experiencing high levels of DBM in recent years
[7, 18–20]. Earlier estimates on DBM in sub-Saharan
Africa reported below 10% prevalence at the household
level [21]. However, more recent studies have reported
a high prevalence of DBM among mother–child pairs
in SSA: overweight/obese mother–stunted child pairs,
13–20% in Kenya [22, 23], 10.3% in Nigeria [24], 14% in
Egypt [21], and 1.8% to 23% in Ethiopia [11, 25, 26].
In Ethiopia, malnutrition affects women and children
disproportionately [27, 28]. Overweight/obesity is rising
rapidly while child undernutrition remains persistent.
e prevalence of stunting, wasting, and being under-
weight were 37%, 21%, and 7%, respectively according to
the 2019 Ethiopian Mini Demographic and Health Sur-
vey report [29]. Compared to the WHO cutoff values
for the significance of undernutrition, the prevalence of
stunting, wasting, and being underweight remained a
serious public problem in the country [30].
Undernutrition has long been considered a major issue
in Ethiopia; overweight and obesity have also been identi-
fied as growing problems [26, 31]. According to a recent
study, 14.9% of women aged 15–49years are overweight
or obese, of which 83.3% were urban dwellers [32]. A
recent systematic review and meta-analysis also reported
that the estimated pooled prevalence of overweight and
obesity among adults in Ethiopia was 20.4% and 5.4%,
respectively [33]. It has been noted that mothers’ over-
weight/obesity is associated with the nutrition transition
situation [34], due to a shift in dietary patterns with pop-
ulations in developing countries consuming more energy
dense than before due to changes in economic condi-
tions. is demonstrated that Ethiopia, like other LMICs,
is subject to the inevitable consequences of DBM; how-
ever, the burden of DBM is still not fully understood [25,
26]. So far, a few studies on overweight and undernutri-
tion coexisting at the household level have been reported
[11, 25, 26, 35].
e DBM at the household level is a complex pub-
lic health problem [36]. e most important contribut-
ing factors of DBM include the place of residence [24,
26], older age of the child (age ≥ 24months) [25, 26, 37],
being a female child [37], maternal older age (age over
30years) [15, 37], household socioeconomic status [25,
38, 39], richest wealth quintile [15], average birth weight
[25], maternal education [15, 37, 38], large family size/
household size [11, 37], and more siblings in the house-
hold [38].
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Sahiledengleetal. Journal of Health, Population and Nutrition (2023) 42:7
Maternal height is a useful indicator for predicting chil-
dren’s risk of developing malnutrition [40–44]. However,
its influences on DBM have not been well investigated.
Only a few studies have shown that DBM is strongly
tied to maternal height [15, 24, 37, 45, 46]. As men-
tioned, a few pocket studies from Mexico [47], Indone-
sia [37], Guatemala [45], and Brazil [48] have examined
the associations and suggest that short maternal height is
associated with a higher risk of DBM. Apart from these
examples, studies on the association between maternal
stature and DBM in developing countries are rare.
To our knowledge, no studies have documented such
an association in Ethiopia. Also, there needs to be more
data that have comprehensively examined household-
level DBM using a large, pooled dataset in Ethiopia.
Previous studies on DBM conducted in Ethiopia have
focused on describing the individual-level DBM [35,
49–51], localized in some areas [11, 35], survey specific
[25, 26], and focus on the coexistence of maternal over-
weight/obesity and child stunting or anemia [52]. Con-
sidering the above, the aims of the present study were to:
(1) determine the prevalence of DBM and (2) examine the
association between maternal stature and DBM among
mother–child pairs in Ethiopia. Given the national and
global targets of achieving food security and improving
maternal and child nutrition, this study is paramount in
providing factual insights regarding the current status of
DBM and designing appropriate preventive strategies in
Ethiopia. Additionally, with these pooled data, we better
understand maternal stature’s influence on the double
burden of malnutrition.
Methods
Data sources andsampling design
is study utilized data from the four consecutive
Ethiopia Demographic and Health Survey (EDHS)
(2000–2016), a nationally representative cross-sectional
household survey [53–56]. Pooled data on mother–
child pairs from the EDHS were included in the study,
to explore the prevalence of double burden of malnu-
trition (DBM). is pooled data analysis also increased
the study power, which allowed a full exploration of the
effect of maternal height on DBM. In the EDHS, ever-
married women aged 15–49years were interviewed for
data on women and children (0–59 months). e sur-
vey was designed to be representative at both national
and regional levels. e EDHS sampling and household
listing methods have been described elsewhere [56].
We used anthropometric indices such as height-for-
age, weight-for-height, and weight-for-age to evalu-
ate children’s nutritional status below 5 years of age
(0–59months). In addition, the study used the women’s
body mass index (BMI) according to WHO cutoff values
[57]. Maternal body mass index (BMI) was classified as
underweight (< 18.5kg/ m2), normal (18.5 to < 24.99kg/
m2), or overweight/obesity ≥ 25.0kg/m2).
e EDHS collected data on the nutritional status
of children by measuring the weight and height of chil-
dren under the age of 5 years in all sampled households,
regardless of whether their mothers were interviewed in
the survey or not. Weight was measured with an elec-
tronic mother–infant scale (SECA 878 flat) designed for
mobile use [56]. Height was measured with a measuring
board (ShorrBoard®). Children younger than 24months
were measured lying down on the board (recumbent
length), while standing height was measured for all older
children.
e three child anthropometric indices used in this
study were calculated using growth standards published
by the World Health Organization (WHO) in 2006 [58].
e height-for-age index is an indicator of linear growth
retardation and cumulative growth deficits in chil-
dren. Children with height-for-age Z-score below minus
two standard deviations (− 2 SD) from the median of
the WHO reference population are stunted or chroni-
cally malnourished. e weight-for-height index meas-
ures body mass in relation to body height or length and
describes current nutritional status. Children whose
Z-score is below minus two standard deviations (− 2
SD) from the median of the reference population are
considered thin (wasted), or acutely undernourished.
Weight-for-age is a composite index of height-for-age
and weight-for-height that accounts for both acute and
chronic undernutrition. Children whose weight-for-age
Z-score is below minus two standard deviations (− 2 SD)
from the median of the reference population are classi-
fied as underweight [58].
Outcome variable
e primary outcome of this study was DBM, derived
from three child anthropometric indices (stunting, wast-
ing, and underweight) and the body mass index (BMI) of
their respective mothers. Height-for-age (HAZ), weight-
for-height (WHZ), and weight-for-age (WAZ) z-scores
below − 2 SD of the WHO Child Growth Standard were
used to define stunting, wasting, and underweight,
respectively [58]. A child who was either stunted, wasted,
or underweight and the mother is over-nourished (over-
weight/ obese) in the same household was considered as
having DBM, as used in past studies [15, 25, 59]. Follow-
ing previous studies, the binary response variable DBM
was measured using “normal” and “DBM” response cat-
egories. Additionally, the prevalence of overweight/obese
mothers and stunted children, overweight/obese mother
and wasted children, and overweight/obese mother and
underweight child was estimated.
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Sahiledengleetal. Journal of Health, Population and Nutrition (2023) 42:7
Main exposure
e main exposure of our study was maternal height. We
adopted height cutoffs used by previous studies [37, 60,
61], but subdivided them into three categories. Accord-
ingly, we categorized maternal height as: very short
(< 145.0cm), short (145.0 to 154.9cm) and normal/tall
(≥ 155.0cm).
Control variables
Covariates were considered based on the availability of
data and previous literature [15, 25, 47, 62–65]. In this
study, we included two levels of confounding variables:
individual (i.e., child, maternal, and household factors)
and community levels. e individual-level covariates
included: child factors (child’s age in months, gender,
birth order, birth interval, size of child at birth, diar-
rhea, fever, and ARI), maternal factors (mother’s age,
mother’s education, mother’s occupation, ANC visit,
anemia status, listening to the radio, and watching tel-
evision), and household-level covariates (wealth index,
household size, type of cooking fuel, toilet facility, source
of drinking water, household flooring, and time to get
a water source). Lastly, the community-level factors
include the place of residence (urban or rural) and con-
textual region of residence (agrarian, pastoralist, and city
administration).
Data analysis
All analyses were carried out using STATA/MP version
14.1 (StataCorp, College Station, TX, USA). e survey
command (svy) in STATA was used to take into account
the sampling design of the survey. Sampling weighting
was applied to all descriptive statistics to compensate
for the disproportionate allocation of the sample. e
weighting technique is explained in full in the EDHS
report [56]. Descriptive statistics such as frequencies and
percentages were used to present the distribution of all
variables.
Given the hierarchical nature of the EDHS data, a mul-
tilevel binary logistic regression model was fitted to esti-
mate the association between DBM and maternal height.
In this model-building process, we first performed an
unadjusted bivariable multilevel analysis between DBM
and exposure or each of the covariates. Variables in
bivariable analysis with a p value < 0.2 were entered in
the multilevel multivariable binary logistic regression
models. All independent variables associated with the
DBM were tested for multicollinearity and there was
no evidence of multicollinearity. Following the recom-
mendations of a previous study, five hierarchical models
were run [66–68]. Accordingly, five models were fitted:
the empty model without any explanatory variable was
run to detect the presence of a possible contextual effect
(Model I), Model II (Model I + child characteristics),
Model III (Model II + mothers characteristics), Model IV
(Model III + household characteristics), Model V (Model
IV + community-level characteristics) were fitted. In our
analysis, all models assumed a random intercept. Model
comparisons were done using the deviance information
criteria (DIC) and the model with the lowest DIC value
was chosen as the best-fitted model for the data. e
intraclass correlation coefficient (ICC) was computed for
each model to show the amount of variations explained
at each level of modeling. e adjusted odds ratio (AOR)
with a 95% confidence interval (CI) and p value < 0.05 in
the Model V multivariable model were used to declare
significant determinants of DBM and its association with
maternal height. Finally, after controlling all covariates
and exposure variables the mean value estimates were
presented (the estimates are obtained after the post-esti-
mation command) using the figure representing the pre-
dictive probability for DBM and maternal height.
Ethical consideration
e data used in this study were obtained from the
MEASURE DHS Program, and permission for data access
was obtained from the measure DHS program through
an online request from http:// www. dhspr ogram. com. e
data used for this study were publicly available with no
personal identifier. ere was no need for ethical clear-
ance as the researcher did not interact with respondents.
Result
Characteristics ofstudy participants
Table1 presents the frequency and the weighted distribu-
tion of DBM, overweight/obesity mother–stunted child,
overweight/obesity mother–wasted child, overweight/
obesity mother–underweight child, and covariates in the
study population. In this study, we analyzed data from a
total of 33,454 mother–child pairs among whom there
were 20,417 (61.0%) normal/tall (≥ 155.0 cm) moth-
ers, 12,265 (36.7%) short (145–155 cm) mothers, and
771 (2.3%) mothers were of very short (< 145.0cm) stat-
ure. e mean maternal height was (156.69cm ± 6.34).
Almost 4 in 10 children belong to the age-group of
36–59 months. Most of the children resided in rural
areas (89.2%).
Prevalence ofmalnutrition
e prevalence of malnutrition is reported in Table2.
e prevalence of stunting, wasting, and being under-
weight among under-five children in Ethiopia is 47.31%
(95% CI 46.77–47.84), 10.95% (95% CI 10.62–11.29), and
31.51% (95% CI 31.01–32.01), respectively. e preva-
lence of overweight/obese mothers was 3.21% (95% CI
3.03–3.40).
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Sahiledengleetal. Journal of Health, Population and Nutrition (2023) 42:7
Table 1 Socio‐demographic characteristics of the sample population and prevalence of mother–child pairs of double burden of
malnutrition by child, maternal, household, and community-level characteristics, EDHS (2000–2016)
Variables Total (n) Percent (%) Overweight/obese
mother–stunted child,
95% CI
Overweight/obese
mother–wasted child,
95% CI
Overweight/obese
mother–underweight
child, 95% CI
DBM, 95% CI
Maternal stature
Normal/tall (≥ 155 cm) 20,417 61.0 1.37 (1.21–1.54) 0.41 (0.32–0.50) 0.71 (0.61–0.84) 1.70 (1.53–1.89)
Short (145 to 154.9 cm) 12,265 36.7 1.51 (1.29–1.77) 0.20 (0.13–0.31) 0.53 (0.41–0.69) 1.72 (1.48–1.98)
Very short (< 145 cm) 771 2.3 5.52 (3.97–7.64) 0.32 (0.08–1.29) 0.30 (0.19–0.47) 5.70 (4.11–7.84)
Individual-level characteristics
Child factors
Sex
Male 17,022 50.9 1.67 (1.48–1.89) 0.39 (0.31–0.51) 0.78 (0.65–0.94) 1.96 (1.75–2.19)
Female 16,431 49.1 1.32 (1.15–1.52) 0.27 (0.20–0.37) 0.62 (0.50–0.75) 1.61 (1.41–1.82)
Age (months)
< 6 3303 9.9 0.53 (0.32–0.86) 0.67 (0.43–1.03) 0.29 (0.15–0.56) 1.14 (0.82–1.59)
6–11 3519 10.5 0.59 (0.38–0.93) 0.56 (0.35–0.89) 0.56 (0.35–0.89) 1.09 (0.78–1.52)
12–23 6519 19.6 0.96 (0.74–1.24) 0.24 (0.15–0.41) 0.36 (0.23–0.55) 1.16 (0.92–1.47)
24–35 6387 19.1 2.07 (1.74–2.46) 0.32 (0.20–0.49) 0.96 (0.74–1.24) 2.31 (1.95–2.72)
36–59 13,620 40.8 1.95 (1.72–2.21) 0.25 (0.18–0.36) 0.88 (0.73–1.06) 2.16 (1.92–2.43)
Birth order
First born 5962 17.8 1.09 (0.86–1.40) 0.24 (0.14–0.40) 0.56 (0.40–0.79) 1.37 (1.10–1.71)
2–4 14,420 43.1 1.41 (1.22–1.62) 0.28 (0.21–0.39) 0.55 (0.43–0.69) 1.63 (1.43–1.86)
5 or higher 13,071 39.1 1.82 (1.58–2.08) 0.44 (0.34–0.58) 0.95 (0.79–1.15) 2.17 (1.92–2.46)
Birth interval
< 33 months 23,093 69.0 1.27 (1.12–1.43) 0.26 (0.21–0.34) 0.58 (0.48–0.69) 1.47 (1.32–1.65)
≥ 33 months 10,360 31.0 2.04 (1.77–2.35) 0.49 (0.37–0.66) 0.98 (0.80–1.20) 2.50 (2.20–2.84)
Size of child at birth
Larger 10,348 31.0 1.58 (1.35–1.86) 0.23 (0.15–0.35) 0.55 (0.42–0.73) 1.79 (1.54–2.08)
Average 13,113 39.3 1.45 (1.25–1.68) 0.37 (0.27–0.49) 0.68 (0.54–0.84) 1.78 (1.55–2.03)
Small 9897 29.7 1.49 (1.26–1.75) 0.41 (0.29–0.56) 0.88 (0.71–1.09) 1.81 (1.55–2.09)
Currently breast-feeding
Ye s 24,827 74.2 1.04 (0.92–1.19) 0.29 (0.23–0.37) 0.53 (0.44–0.64) 1.29 (1.15–1.45)
No 8626 25.8 2.57 (2.26–2.91) 0.44 (0.32–0.60) 1.09 (0.90–1.33) 2.94 (2.6–3.30)
Full vaccination (n = 28,647)
Ye s 5572 19.4 2.15 (1.81–2.55) 0.29 (0.19–0.47) 0.71 (0.53–0.95) 2.43 (2.07–2.85)
No 23,075 80.6 1.15 (1.02–1.31) 0.32 (0.25–0.41) 0.60 (0.50–0.71) 1.41 (1.26–1.59)
Diarrhea
Ye s 5722 17.1 1.21 (0.95–1.55) 0.25 (0.14–0.43) 0.71 (0.51–0.97) 1.43 (1.13–1.79)
No 27,685 82.9 1.56 (1.42–1.72) 0.35 (0.29–0.44) 0.70 (0.61–0.81) 1.86 (1.70–2.04)
Fever
Ye s 6815 20.4 1.21 (0.97–1.51) 0.34 (0.22–0.51) 0.74 (0.64–0.85) 1.50 (1.23–1.83)
No 26,590 79.6 1.57 (1.42–1.74) 0.34 (0.27–0.42) 0.55 (0.39–0.76) 1.85 (1.69–2.04)
ARI
Ye s 1344 4.0 1.78 (1.15–2.74) 0.44 (0.18–1.06) 0.62 (0.29–1.28) 2.23 (1.51–3.28)
No 32,109 96.0 1.49 (1.35–1.63) 0.33 (0.27–0.40) 0.70 (0.62–0.81) 1.77 (1.62–1.93)
Parental factors
Mother’s age
< 18 270 0.8 – 0.41 (0.57–2.87) 0.40 (0.05–2.84) 0.41 (0.05–2.88)
18–24 7774 23.2 0.81 (0.62–1.04) 0.18 (0.10–0.31) 0.41 (0.29–0.59) 0.97 (0.77–1.23)
25–34 16,972 50.7 1.57 (1.38–1.77) 0.32 (0.24–0.42) 0.67 (0.56–0.82) 1.81 (1.61–2.03)
≥ 35 8437 25.2 2.06 (1.76–2.41) 0.52 (0.38–0.72) 1.04 (0.83–1.29) 2.54 (2.21–2.93)
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Sahiledengleetal. Journal of Health, Population and Nutrition (2023) 42:7
Table 1 (continued)
Variables Total (n) Percent (%) Overweight/obese
mother–stunted child,
95% CI
Overweight/obese
mother–wasted child,
95% CI
Overweight/obese
mother–underweight
child, 95% CI
DBM, 95% CI
Mother’s education
No education 24,378 72.9 1.27 (1.13–1.43) 0.27 (0.21–0.35) 0.67 (0.57–0.79) 1.50 (1.35–1.67)
Primary 7358 22.0 1.89 (1.58–2.25) 0.38 (0.26–0.57) 0.72 (0.54–0.96) 2.18 (1.84–2.56)
Secondary 1325 3.9 2.29 (1.69–3.12) 0.57 (0.31–1.06) 0.74 (0.43–1.28) 2.82 (2.14–3.72)
Higher 392 1.2 3.67 (2.38–5.63) 1.47 (0.74–2.92) 1.28 (0.61–2.67) 5.16 (3.59–7.38)
Mother’s occupation
Not working 16,232 48.7 1.58 (1.40–1.79) 0.35 (0.27–0.45) 0.80 (0.67–0.95) 1.89 (1.69–2.12)
Non-agriculture 7011 21.0 2.35 (2.01–2.75) 0.59 (0.42–0.81) 1.02 (0.80–1.29) 2.82 (2.44–3.26)
Agriculture 10,108 30.3 0.66 (0.51–0.86) 0.09 (0.05–0.19) 0.24 (0.15–0.37) 0.75 (0.58–0.96)
Antenatal care (ANC) visit
None 13,080 57.1 1.04 (0.08–1.25) 0.21 (0.14–0.31) 0.50 (0.38–0.65) 1.22 (1.03–1.43)
1–3 5285 23.1 1.57 (1.25–1.97) 0.42 (0.27–0.65) 0.79 (0.57–1.08) 1.96 (1.60–2.40)
4–7 4054 17.7 2.27 (1.87–2.77) 0.69 (0.48–0.99) 0.98 (0.73–1.33) 2.84 (2.38–3.38)
8+494 22.2 3.53 (2.45–5.07) 0.75 (0.34–1.68) 0.88 (0.42–1.84) 4.30 (3.09–5.96)
Any anemia (n = 23,593)
Ye s 6275 26.6 1.64 (1.35–1.98) 0.37 (0.28–0.48) 0.93 (0.72–1.20) 2.07 (1.76–2.44)
No 17,318 73.4 1.64 (1.45–1.86) 0.50 (0.28–0.48) 0.74 (0.61–0.88) 1.94 (1.73–2.17)
Listening to radio
Ye s 11,515 34.4 1.97 (1.72–2.26) 0.44 (0.33–0.59) 0.81 (0.65–1.04) 2.37 (2.09–2.69)
Not at all 21,929 65.6 1.27 (1.12–1.43) 0.28 (0.22–0.36) 0.65 (0.55–0.77) 1.50 (1.34–1.67)
Watching television
Ye s 5865 17.5 2.83 (2.44–3.28) 0.66 (0.48–0.90) 1.04 (0.81–1.33) 3.42 (2.98–3.91)
Not at all 27,568 82.5 1.18 (1.05–1.33) 0.26 (0.20–0.33) 0.62 (0.53–0.73) 1.40 (1.26–1.55)
Household factors
Wealth index
Poor 10,711 45.2 1.15 (0.97–1.37) 0.34 (0.25–0.47) 0.67 (0.54–0.84) 1.46 (1.25–1.70)
Middle 4958 20.9 0.76 (0.52–1.11) 0.17 (0.07–0.37) 0.39 (0.23–0.66) 0.91 (0.64–1.28)
Rich 7999 33.8 2.80 (2.45–3.19) 0.62 (0.47–0.83) 1.14 (0.93–1.41) 3.30 (2.92–3.73)
Household size
1–4 8042 24.0 1.25 (1.03–1.53) 0.23 (0.15–0.37) 0.58 (0.43–0.77) 1.48 (1.23–1.78)
≥ 5 25,411 76.0 1.58 (1.43–1.75) 0.37 (0.30–0.46) 0.74 (0.64–0.86) 1.89 (1.72–2.07)
Type of cooking fuel (n = 32,865)
Clean fuels 32,507 98.9 5.26 (3.68–7.47) 1.09 (0.49–2.41) 1.61 (0.83–3.06) 1.70 (1.56–1.85)
Solid fuels 357 1.1 1.42 (1.29–1.57) 0.32 (0.26–0.39) 0.69 (0.60–0.79) 6.01 (4.30–8.33)
Toilet facility (n = 32, 865)
Improved 3752 11.4 3.28 (2.82–3.81) 0.80 (0.59–1.09) 1.44 (1.14–1.81) 3.91 (3.41–4.48)
Unimproved 10,443 31.8 1.66 (1.39–1.98) 0.34 (0.23–0.51) 0.73 (0.56–0.95) 1.96 (1.67–2.31)
Open defecation 18,669 56.8 0.93 (0.79–1.08) 0.21 (0.15–0.28) 0.49 (0.40–0.61) 1.11 (0.96–1.27)
Source of drinking water (32,858)
Improved 12,667 38.5 2.26 (2.01–2.54) 0.52 (0.40–0.66) 1.05 (0.88–1.25) 2.71 (2.43–3.01)
Unimproved 20,190 61.5 0.99 (0.86–1.15) 0.22 (0.16–0.30) 0.48 (0.39–0.60) 1.17 (1.03–1.34)
Household flooring
Improved 2685 8.0 4.26 (3.68–4.94) 1.05 (0.73–1.36) 1.64 (1.29–2.88) 5.05 (4.41–5.78)
Unimproved 30,761 92.0 1.08 (0.97–1.22) 0.24 (0.18–0.30) 0.56 (0.47–0.66) 1.29 (1.17–1.44)
Time to get a water source
On–premise 1744 5.2 4.06 (3.35–4.90) 1.14 (0.79–1.64) 1.71 (1.27–2.29) 5.07 (4.28–6.0)
≤ 30 min 19,373 58.3 1.26 (1.10–1.45) 0.19 (0.14–0.28) 0.58 (0.47–0.71) 1.44 (1.27–1.64)
31–60 min 6840 20.6 1.14 (0.90–1.45) 0.32 (0.21–0.51) 0.55 (0.39–0.78) 1.37 (1.10–1.71)
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Page 7 of 16
Sahiledengleetal. Journal of Health, Population and Nutrition (2023) 42:7
Prevalence ofdouble burden ofmalnutrition
Table2 also presents the weighted prevalence of differ-
ent forms of the double burden of malnutrition. e
prevalence of overweight/obesity mother and stunted
children was 1.31% (95% CI 1.19–1.44), while the preva-
lence of overweight/obesity mother and the wasted child
was 0.23% (95% CI 0.18–0.28) and that of overweight/
obese mothers and the underweight children was 0.58%
(95% CI 0.51–0.66). Overall, the prevalence of DBM was
found to be 1.52% (95% CI 1.39–1.65). e prevalence of
DBM was significantly higher (5.7%) among the children
of women with very short maternal height (< 145 cm).
e highest prevalence of the DBM (2.31%, 95% CI
1.95–2.72) occurred among children aged 24–35months.
DBM was highest among women over 35years (2.54%,
95% CI 2.21–2.93) of age than women in any other age-
group. e prevalence of DBM was higher among urban
residents (4.74% vs 1.21%).
Association ofDBM andmaternal stature
The unadjusted association between DBM and expo-
sure and other study covariates are given in Table3.
The association between DBM and maternal very
short height (< 145 cm) was highly significant (p
value < 0.001) in the unadjusted model. Tables4 and 5
present the adjusted odds ratio (AOR) with a 95% CI
of DBM and maternal height. Our results showed that
DBM was positively associated with maternal height
adjusted for individual (i.e., child, maternal, house-
hold) and community-level covariates. The adjusted
Table 1 (continued)
Variables Total (n) Percent (%) Overweight/obese
mother–stunted child,
95% CI
Overweight/obese
mother–wasted child,
95% CI
Overweight/obese
mother–underweight
child, 95% CI
DBM, 95% CI
> 60 min 5291 15.9 1.22 (0.97–1.54) 0.33 (0.21–0.52) 0.68 (0.50–0.92) 1.49 (1.22–1.84)
Community-level characteristics
Residence
Urban 3612 10.8 3.99 (3.48–4.57) 1.03 (0.78–1.35) 1.66 (1.34–2.05) 4.74 (4.18–5.36)
Rural 29,841 89.2 1.01 (0.89–1.14) 0.20 (0.15–0.26) 0.51 (0.43–0.61) 1.21 (1.08–1.35)
Region
Agrarian 18,220 54.5 1.31 (1.15–1.48) 0.33 (0.25–0.42) 0.69 (0.58–0.82) 1.56 (1.39–1.75)
Pastoralist 14,450 43.2 1.11 (0.91–1.37) 0.19 (0.11–0.31) 0.56 (0.41–0.75) 1.30 (1.07–1.58)
City administration 783 2.3 2.96 (2.50–3.50) 0.62 (0.43–0.90) 0.99 (0.74–1.32) 3.52 (3.02–4.10)
Survey year
EDHS-2000 9785 29.2 1.11 (0.91–1.35) 0.15 (0.08–0.26) 0.46 (0.34–0.63) 1.23 (1.02–1.49)
EDHS-2005 4282 12.8 1.46 (1.12–1.89) 0.26 (0.14–0.48) 0.67 (0.46–0.99) 1.75 (1.38–2.22)
EDHS-2011 9989 29.8 1.44 (1.22–1.70) 0.28 (0.19–0.41) 0.62 (0.48–0.81) 1.65 (1.41–1.92)
EDHS-2016 9340 28.1 1.96 (1.69–2.27) 0.61 (0.46–0.79) 1.02 (0.83–1.25) 2.49 (2.18–2.84)
All values were weighted
Table 2 Prevalence of malnutrition and double burden of malnutrition (DBM) at household level in Ethiopia, EDHS (2000–2016)
All values were weighted
Levels of malnutrition Frequency Prevalence (%) 95% CI
Stunted child (n = 33,564) 15,878 47.31 46.77–47.84
Wasted child (n = 33,583) 3679 10.95 10.62–11.29
Underweight child (n = 33,729) 10,627 31.51 31.01–32.01
Overweight/obesity mother (n = 34,441) 1105 3.21 3.03–3.40
Double burden of malnutrition at household level
Overweight/obesity mother and stunted child (n = 33,547) 439 1.31 1.19–1.44
Overweight/obesity mother and wasted child (n = 33,566) 77 0.23 0.18–0.28
Overweight/obesity mother and underweight child (n = 33,711) 194 0.58 0.51–0.66
Overweight/obesity mother and stunted or wasted or underweight child
(n = 33,454) 508 1.52 1.39–1.65
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Page 8 of 16
Sahiledengleetal. Journal of Health, Population and Nutrition (2023) 42:7
Table 3 Unadjusted association between double burden
of malnutrition (DBM) and maternal heights and other study
covariates among mother–child pairs in Ethiopia, EDHS (2000–
2016)
Variables DBM
Weighted
prevalence
(95% CI)
Unadjusted OR,
95% CI p value
Maternal stature
Normal/tall
(≥ 155 cm) 1.70 (1.53–1.89) Ref
Short (145 to
154.9 cm) 1.72 (1.48–1.98) 1.05 (0.87–1.26) 0.610
Very short (< 145 cm) 5.70 (4.11–7.84) 3.76 (2.59–5.44) < 0.001
Individual-level characteristics
Child factors
Sex
Male 1.96 (1.75–2.19) Ref
Female 1.61 (1.41–1.82) 0.81 (0.68–0.96) 0.018
Age (months)
< 6 1.14 (0.82–1.59) 0.52 (0.36–0.74) < 0.001
6–11 1.09 (0.78–1.52) 0.49 (0.35–0.71) < 0.001
12–23 1.16 (0.92–1.47) 0.53 (0.41–0.69) < 0.001
24–35 2.31 (1.95–2.72) 1.07 (0.87–1.32) 0.517
36–59 2.16 (1.92–2.43) Ref
Birth order
First born 1.37 (1.10–1.71) 0.61 (0.47–0.78) < 0.001
2–4 1.63 (1.43–1.86) 0.74 (0.61–0.89) 0.001
5 or higher 2.17 (1.92–2.46) Ref
Birth interval
< 33 months 1.47 (1.32–1.65) Ref
≥ 33 months 2.50 (2.20–2.84) 1.74 (1.46–2.07) < 0.001
Size of child at birth
Larger 1.79 (1.54–2.08) Ref
Average 1.78 (1.55–2.03) 1.01 (0.81–1.23) 0.979
Small 1.81 (1.55–2.09) 1.03 (0.82–1.28) 0.796
Currently breast-feeding
Ye s 1.29 (1.15–1.45) Ref
No 2.94 (2.6–3.30) 2.26 (1.90–2.69) < 0.001
Full vaccination
Ye s 2.43 (2.07–2.85) Ref
No 1.41 (1.26–1.59) 0.58 (0.47–0.71) < 0.001
Diarrhea
Ye s 1.43 (1.13–1.79) 0.77 (0.60–0.99) 0.045
No 1.86 (1.70–2.04) Ref
Fever
Ye s 1.50 (1.23–1.83) 0.82 (0.65–1.02) 0.076
No 1.85 (1.69–2.04) Ref
ARI
Ye s 2.23 (1.51–3.28) 1.28 (0.85–1.94) 0.234
No 1.77 (1.62–1.92) Ref
Table 3 (continued)
Variables DBM
Weighted
prevalence
(95% CI)
Unadjusted OR,
95% CI p value
Maternal factors
Mother’s age
< 18 0.41 (0.05–2.88) 0.16 (0.02–1.12) 0.065
18–24 0.97 (0.77–1.23) 0.37 (0.28–0.49) < 0.001
25–34 1.81 (1.61–2.03) 0.69 (0.58–0.84) < 0.001
≥ 35 2.54 (2.21–2.93) Ref
Mother’s education
No education 1.51 (1.35–1.67) 0.61 (0.51–0.73) < 0.001
Primary and
above 2.49 (2.18–2.82) Ref
Mother’s occupation
Not working 1.89 (1.69–2.12) Ref
Non-agriculture 2.82 (2.44–3.26) 1.49 (1.23–1.80) < 0.001
Agriculture 0.75 (0.58–0.96) 0.39 (0.30–0.53) < 0.001
Antenatal care (ANC) visit
None 1.22 (1.03–1.43) Ref
1–3 1.96 (1.60–2.40) 1.62 (1.24–2.11) < 0.001
4–7 2.84 (2.38–3.38) 2.36 (1.84–3.02) < 0.001
8+4.30 (3.09–5.96) 3.59 (2.43–5.29) < 0.001
Listening to radio
Ye s 2.37 (2.09–2.69) Ref
Not at all 1.50 (1.34–1.67) 0.63 (0.53–0.75) < 0.001
Watching television
Ye s 3.42 (2.98–3.91) Ref
Not at all 1.40 (1.26–1.55) 0.41 (0.34–0.49) < 0.001
Household factors
Wealth index
Poor 1.46 (1.25–1.70) Ref
Middle 0.91 (0.64–1.28) 0.61 (0.42–0.90) 0.014
Rich 3.30 (2.92–3.73) 2.19 (1.77–2.70) < 0.001
Household size
1–4 1.48 (1.23–1.78) 0.77 (0.62–0.94) 0.014
≥ 5 1.89 (1.72–2.07) Ref
Type of cooking fuel
Clean fuels 1.70 (1.56–1.85) Ref
Solid fuels 6.01 (4.30–8.33) 0.27 (0.18–0.39) < 0.001
Toilet facility
Improved 3.91 (3.41–4.48) Ref
Unimproved 1.96 (1.67–2.31) 0.52 (0.41–0.65) < 0.001
Open defecation 1.11 (0.96–1.27) 0.29 (0.23–0.35) < 0.001
Source of drinking water
Improved 2.71 (2.43–3.01) Ref
Unimproved 1.17 (1.03–1.34) 0.43 (0.36–0.51) < 0.001
Household flooring
Improved 5.05 (4.41–5.78) 3.88 (3.22–4.68) < 0.001
Unimproved 1.29 (1.17–1.44) Ref
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Page 9 of 16
Sahiledengleetal. Journal of Health, Population and Nutrition (2023) 42:7
multilevel models estimated that compared to the
children of tall mothers (height ≥ 155 cm), the odds
of DBM was 1.37 times higher among children whose
mothers’ height ranged from 145 to 155 cm (AOR:
1.37, 95% CI 1.04–1.80). The odds of DBM was 2.98
times higher among children whose mothers had short
stature (height < 145 cm) (AOR: 2.98, 95% CI 1.52–
5.86) compared to children whose mothers had tall
stature (height ≥ 155cm).
Table5 summarizes unadjusted, adjusted odds ratios
and absolute probability of DBM. Marginal effects
show the change in probability when the predictor
or independent variable increases by one unit. The
change in probability of DBM when maternal height
goes from normal to short increases by 2.2 percent-
age and is significant. Similarly, the change in prob-
ability when maternal height goes from short to very
short increases by 4.6 percentage points, which is also
significant.
Figure1 shows the predicted probabilities along with
their 95% confidence interval. The predicted probabil-
ity of DBM was on the “Y axis” and maternal height
was on the “X axis.” The fitted line increases from right
to left, indicating that as maternal height decreases
from normal to very short, the probability of DBM
increases.
Discussion
e concept of the double burden of malnutrition
(DBM) at the household level is not well understood
in Ethiopia. To our knowledge, this is the first compre-
hensive assessment undertaken: (a) to determine the
prevalence of DBM and (b) to examine the associations
between DBM and maternal height in Ethiopia. e
prevalence of DBM was below 2%. Our results showed
that DBM was strongly associated with maternal height
after adjusting for potential individual and community-
level covariates.
In this study, the overall prevalence of DBM was 1.52%.
However, the DBM increased with age in women after
35 years and increased with urbanization. e current
finding in India indicates a rising concern about DBM
as the country goes through a perfect wave of changes
in dietary patterns and physical activity due to urbaniza-
tion and economic development [69]. Ethiopia is imple-
menting policies that will allow the country to achieve
lower-middle-income status. Henceforth, the coexist-
ence of multiple forms of malnutrition in households will
likely increase in the coming years. e double burden of
malnutrition has also been linked to a high level of food
insecurity and a higher prevalence of infection, combined
with rapid population growth and urbanization, which
may lead to an increase in the prevalence of DBM [19].
e existence of a double burden of malnutrition in the
same household was reported in different low-income
settings such as in Bangladesh [16, 70], Indonesia [37],
Kenya [22], Nepal [15], and India [59]. Few studies have
also reported Ethiopia’s household-level double burden
of malnutrition [11, 25, 26]. e observed prevalence of
DBM was lower than the finding from a study in Nepal,
6.6% [15], and studies from Bangladesh, 6·3%, [70] and,
4.9%, [71]. e low-level prevalence of DBM might be
due to the low proportion of women who are overweight
or obese in the country. In Ethiopia, however, the propor-
tion of women who are overweight or obese has increased
over time from 3% in 2000 to 8% in 2016 [56]. In the same
period, however, the prevalence of overweight/obesity
increased from 6.5 to 22.1% between 2001 and 2016
among women of reproductive age (15–49years) [72] in
Nepal. In Bangladesh, the prevalence of overweight was
about 29% and the rate of obesity was approximately 11%
among women of reproductive age [73]. Another study
from Bangladesh reported increases in the prevalence of
overweight and obesity from 2004 to 2014 as follows: the
prevalence of overweight increased from 11.4% in 2004
to 25.2% in 2014, and the prevalence of obesity increased
from 3.5% to 11.2% over the same period of time [74].
e observed increase in mothers’ overweight or obesity
was stated to be associated with the nutrition transition
situation [34].
Table 3 (continued)
Variables DBM
Weighted
prevalence
(95% CI)
Unadjusted OR,
95% CI p value
Time to get a water source
On-premise 5.07 (4.28–6.0) Ref
≤ 30 min 1.44 (1.27–1.64) 0.28 (0.22–0.35) < 0.001
31–60 min 1.37 (1.10–1.71) 0.27 (0.19–0.36) < 0.001
> 60 min 1.49 (1.22–1.84) 0.29 (0.22–0.39) < 0.001
Community-level characteristics
Residence
Urban 4.74 (4.18–5.36) 4.07 (3.39–4.89) < 0.001
Rural 1.21 (1.08–1.35) Ref
Region
Agrarian 1.56 (1.39–1.75) Ref
Pastoralist 1.30 (1.07–1.58) 0.79 (0.62–1.01) 0.059
City administra-
tion 3.52 (3.02–4.10) 2.33 (1.88–2.89) < 0.001
Survey year
EDHS-2000 1.23 (1.02–1.49) 0.48 (0.38–0.61) < 0.001
EDHS-2005 1.75 (1.38–2.22) 0.68 (0.51–0.91) 0.008
EDHS-2011 1.65 (1.41–1.92) 0.66 (0.53–0.82) < 0.001
EDHS-2016 2.49 (2.18–2.84) Ref
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Page 10 of 16
Sahiledengleetal. Journal of Health, Population and Nutrition (2023) 42:7
Table 4 Adjusted odds ratio estimates on the association between maternal heights and double burden of malnutrition (DBM)
among mother–child pairs in Ethiopia, EDHS (2000–2016)
Variables Model 0: without
independent
variables
Model 1: Maternal
stature and child
characteristics AOR
(95% CI)
Model 2: model
1 + maternal
characteristics AOR
(95% CI)
Model 3: model
2 + household
characteristics AOR
(95% CI)
Model 4: model
3 + community-level
factors AOR (95% CI)
Individual-level characteristics
Maternal stature
Normal/tall
(≥ 155.0 cm) Ref Ref Ref Ref
Short (145 to 154.9 cm) 1.08 (0.87–1.33) 1.26 (0.99–1.60) 1.35 (1.03–1.78)* 1.37 (1.04–1.80)*
Very short (< 145.0 cm) 4.05 (2.64–6.19)** 4.03 (2.41–6.72)** 2.94 (1.49–5.77)* 2.95 (1.50–5.80)*
Child factors
Sex
Male Ref Ref Ref Ref
Female 0.81 (0.66–0.98)* 0.83 (0.66–1.04) 0.78 (0.61–1.02) 0.77 (0.60–1.01)
Age (months)
< 6 0.98 (0.65–1.47) 0.86 (0.52–1.42) 0.66 (0.37–1.18) 0.59 (0.32–1.05)
6–11 0.91 (0.61–1.35) 0.82 (0.50–1.33) 0.61 (0.35–1.06) 0.53 (0.30–0.95)*
12–23 0.81 (0.59–1.09) 0.74 (0.50–1.09) 0.63 (0.41–0.99)* 0.55 (0.34–0.88)*
24–35 1.33 (1.05–1.69)* 1.27 (0.93–1.73) 1.08 (0.75–1.54) 0.95 (0.65–1.40)
36–59 Ref Ref Ref Ref
Birth order
First born 0.81 (0.58–1.13) 0.81 (0.52–1.25) 0.65 (0.37–1.14) 0.64 (0.37–1.11)
2–4 0.91 (0.71–1.18) 0.84 (0.62–1.14) 0.77 (0.53–1.01) 0.75 (0.52–1.08)
5 or higher Ref Ref Ref Ref
Birth interval
< 33 months Ref Ref Ref Ref
≥ 33 months 1.46 (1.13–1.88)* 1.10 (0.66–1.82) 1.17 (0.68–2.00) 1.19 (0.69–2.04)
Currently breast-feeding
Ye s Ref Ref Ref Ref
No 1.89 (1.52–2.35)** 1.64 (1.22–2.19)* 1.56 (1.12–2.19)* 1.56 (1.12–2.19)*
Full vaccination
Ye s Ref Ref Ref Ref
No 0.62 (0.50–0.77)** 1.63 (1.22–2.19)* 0.99 (0.72–1.36) 1.03 (0.75–1.42)
Diarrhea
Ye s 0.90 (0.68–1.20) 0.87 (0.64–1.20) 0.78 (0.53–1.16) 0.79 (0.53–1.18)
No Ref Ref Ref Ref
Fever
Ye s 0.85 (0.65–1.10) 0.98 (0.74–1.31) 1.07 (0.76–1.52) 1.09 (0.76–1.54)
No Ref Ref Ref Ref
ARI
Ye s 1.45 (0.91–2.33) 1.17 (0.68–2.04) 1.22 (0.69–2.17) 1.29 (0.72–2.29)
No Ref Ref Ref Ref
Maternal factors
Mother’s age
< 18 0.29 (0.03–2.34) 0.69 (0.08–5.60) 0.76 (0.09–6.23)
18–24 0.41 (0.22–0.78)* 0.47 (0.23–0.97)* 0.49 (0.24–1.02)
25–34 0.75 (0.45–1.24) 0.91 (0.53–1.57) 0.94 (0.54–1.61)
≥ 35 Ref Ref Ref
Mother’s education
No education Ref Ref Ref
Primary and above 1.27 (0.96–1.69) 1.01 (0.72–1.40) 0.98 (0.70–1.38)
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Page 11 of 16
Sahiledengleetal. Journal of Health, Population and Nutrition (2023) 42:7
Table 4 (continued)
Variables Model 0: without
independent
variables
Model 1: Maternal
stature and child
characteristics AOR
(95% CI)
Model 2: model
1 + maternal
characteristics AOR
(95% CI)
Model 3: model
2 + household
characteristics AOR
(95% CI)
Model 4: model
3 + community-level
factors AOR (95% CI)
Mother’s occupation
Not working Ref Ref Ref
Non-agriculture 1.13 (0.87–1.45) 0.99 (0.74–1.34) 0.97 (0.72–1.31)
Agriculture 0.40 (0.27–0.58)** 0.58 (0.37–0.92)* 0.61 (0.39–0.97)*
Antenatal care (ANC) visit
None Ref Ref Ref
1–3 1.42 (1.05–1.93)* 1.22 (0.85–1.75) 1.12 (0.78–1.62)
4–7 1.56 (1.13–2.17)* 1.24 (0.85–1.83) 1.10 (0.74–1.63)
8+1.85 (1.15–3.00)* 1.21 (0.68–2.16) 1.08 (0.60–1.94)
Listening to radio
Ye s Ref Ref Ref
Not at all 0.87 (0.67–1.13) 1.05 (0.78–1.41) 0.97 (0.72–1.33)
Watching television
Ye s Ref Ref Ref
Not at all 0.63 (0.47–0.85)* 1.22 (0.83–1.78) 1.26 (0.84–1.87)
Household factors
Wealth index
Poor Ref Ref
Middle 0.53 (0.30–0.94)* 0.55 (0.32–0.98)*
Rich 1.15 (0.75–1.74) 1.08 (0.71–1.67)
Household size
1–4 0.99 (0.70–1.39) 0.95 (0.67–1.34)
≥ 5 Ref Ref
Type of cooking fuel
Clean fuels Ref Ref
Solid fuels 0.92 (0.53–1.59) 1.06 (0.61–1.86)
Toilet facility
Improved Ref Ref
Unimproved 0.92 (0.64–1.32) 0.95 (0.65–1.37)
Open defecation 0.65 (0.41–1.02) 0.68 (0.43–1.07)
Source of drinking water
Improved Ref Ref
Unimproved 0.75 (0.55–1.03) 0.80 (0.57–1.12)
Household flooring
Improved 1.87 (1.23–2.84)* 1.60 (1.04–2.47)*
Unimproved Ref Ref
Time to get a water source
On-premise Ref Ref
≤ 30 min 0.59 (0.39–0.89)* 0.71 (0.46–1.08)
31–60 min 0.57 (0.35–0.96)* 0.70 (0.42–1.18)
> 60 min 0.72 (0.44–1.19) 0.88 (0.53–1.48)
Community-level characteristics
Residence
Urban 1.75 (1.11–2.77)*
Rural Ref
Region
Pastoralist Ref
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 12 of 16
Sahiledengleetal. Journal of Health, Population and Nutrition (2023) 42:7
As documented in the current study, the prevalence of
DBM is low in Ethiopia compared to other related low-
income settings. Another possible explanation for this
phenomenon relates to differences in the urbanization of
society, maternal nutrition status, socioeconomic devel-
opment, and sociocultural factors, which may lead to
alterations in food consumption habits and accelerate the
occurrence of DBM.
Prior evidence from 126 low-income and middle-
income countries (LMICs) revealed that the prevalence
of household-level DBM ranged from 3 to 35%, with
the most prevalent being maternal overweight/obesity
and child stunting [2]. In our analysis, the prevalence of
overweight/obese mother–stunted child pairs was 1.31%.
e prevalence was closely comparable with a study find-
ing from Nepal 1.54% [14]. However, our finding was
much lower than the prevalence rates reported from
Bangladesh at 4.10%, Pakistan at 3.93%, and Myanmar
at 5.54% [14]. Additionally, a much higher prevalence of
overweight/obese mother–stunted child was observed
in Bangladesh at 24.5% [16], in Benin at 11.5% [75], and
in Kenya at 20% [22]. Similarly, the prevalence of over-
weight/obese mothers and wasted or underweight chil-
dren was lower than in related studies from Kenya [22]
and Bangladesh [70]. e low prevalence of overweight/
obese mothers with stunted children in Ethiopia could be
Table 4 (continued)
Variables Model 0: without
independent
variables
Model 1: Maternal
stature and child
characteristics AOR
(95% CI)
Model 2: model
1 + maternal
characteristics AOR
(95% CI)
Model 3: model
2 + household
characteristics AOR
(95% CI)
Model 4: model
3 + community-level
factors AOR (95% CI)
Agrarian 1.11 (0.77–1.59)
City administration 1.18 (0.74–1.87)
Survey year
EDHS-2000 0.81 (0.63–1.93)
EDHS-2005 0.78 (0.51–1.21)
EDHS-2011 0.67 (0.48–0.93)*
EDHS-2016 Ref
Random effect
Variance (SE) 0.5259 (0.0052)** 0.5844 (0.0068)** 0.2060 (0.0169)* 0.0629 (0.0797)* 0.04608 (0.1064)*
Log-likelihood ratio
(LL) − 2718.321 − 2105.148 − 1514.540 − 1113.319 − 1107.582
Deviance 5436.642 4210.297 3029.08 2226.63 2215.16
ICC (%) 13.78 15.08 5.89 1.87 1.38
AIC 5440.64 4244.297 3085.08 2304.64 2303.16
BIC 5457.30 4383.264 3305.03 2596.368 2632.29
EDHS Ethiopian Demographic and Health Survey, AIC Akaike’s information criterion, BIC Bayesian information criterion, ICC Inter-cluster correlation, AOR Adjusted
odds ratio
*p < 0.005; **p < 0.001
Table 5 Summary of the association between maternal height and double burden of malnutrition (unadjusted and adjusted odds
ratios with 95% CI and absolute probabilities with 95% CI)
a Model adjusted for individual- and community-level variables
*p < 0.05
DBM, 95% CI Unadjusted Adjusteda
Unadjusted OR, 95% CI Absolute
probabilities (95%
CI)
Adjusted OR, 95% CI Absolute
probabilities
(95% CI)
Maternal stature
Normal/tall (≥ 155 cm) 1.70 (1.53–1.89) Ref 0.017 (0.015–0.018) Ref 0.016 (0.014–0.019)
Short (145 to 154.9 cm) 1.72 (1.48–1.98) 1.05 (0.87–1.26) 0.017 (0.014–0.019) 1.37 (1.04–1.80)* 0.022 (0.018–0.027)
Very short (< 145 cm) 5.70 (4.11–7.84) 3.76 (2.59–5.44)* 0.057 (0.038–0.075) 2.98 (1.52–5.86)* 0.046 (0.019–0.073)
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 13 of 16
Sahiledengleetal. Journal of Health, Population and Nutrition (2023) 42:7
attributed to the high burden of stunted children in rural
areas and the high prevalence of maternal overweight/
obesity in urban areas. According to the most recent 2019
Ethiopian Mini Demographic and Health Survey, child-
hood stunting remained stagnant at 37% (74.65% live in
rural areas) and 12% of children under age 5 are severely
stunted [29]. e majority of overweight or obese women
are also found in urban areas, and the prevalence of over-
weight/obesity has increased significantly from 10.9% in
2000 to 21.4% in 2016 [31]. It is also worth noting that
policy differences, as well as other commitments to com-
bating malnutrition, as well as factors such as maternal
nutrition status, socioeconomic development, and socio-
cultural factors, may account for variations in the preva-
lence of DBM.
It has already been reported that maternal stature is
linked with adverse child and maternal health outcomes
[44, 60, 61, 76–79]. Several studies have also examined
the association of maternal height with child malnutri-
tion [40, 42, 43, 80]. In our analysis, DBM was signifi-
cantly associated with the mother’s height. e adjusted
models estimated that compared to the children of tall
mothers (height ≥ 155.0 cm), the odds of DBM signifi-
cantly increased by about 1.37 times among the children
of the mothers with 145.0 to 155.0cm height. Similarly,
the odds of DBM was 2.98 times higher for the children
of the very shortest mothers (height < 145.0 cm) com-
pared to the children of tall mothers (height ≥ 155.0cm).
is result is consistent with Sunuwar etal., finding in
Nepal [15] which reported that short stature in mothers
was strongly associated with the risk of DBM compared
to mothers of normal height. ese linkages also align
with previous studies from Indonesia and Bangladesh
[37], Mexico [47], Guatemala [81], and Brazil [48]
reported that short maternal stature increases the risk of
the double burden of malnutrition. Several factors and
pathways may have contributed to and explained this
association: (1) Body mass index (BMI) gain was signifi-
cantly higher in short-statured women [82], (2) women
of short stature are more likely to have undernourished
children than women of normal stature [34, 76], (3)
maternal height influences offspring linear growth over
the growing period [42], (4) it has been also noted that
women with short stature were more likely to suffer from
chronic degenerative diseases and subsequently have
stunted children than the women of normal stature [34],
and (5) stunting is an intergenerational phenomenon
passed down from mother to child and contributes to
small for gestational age babies. As a result, being a very
short or short mother may have carried one or more of
the identified risks amplifying the likelihood of experi-
encing the DBM. is study highlights the importance of
developing programs and policies that address the nutri-
tion needs of short-statured mothers in order to break
the vicious intergenerational cycle of malnutrition under
the same roof.
e strength of this study lies in the robust analytical
and statistical methods used. Our findings contribute
significantly to knowledge by being the first to inves-
tigate the relationship between maternal stature and
DBM in the Ethiopian context. Additionally, because
we used a nationally representative dataset, the findings
of this study are generalizable to similar low-income
settings. Nonetheless, our study has some limitations,
and the findings should be interpreted with caution.
First, the nutritional status of the mother was assessed
Fig. 1 Unadjusted (A) and adjusted (B) absolute probabilities of DBM and maternal height
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 14 of 16
Sahiledengleetal. Journal of Health, Population and Nutrition (2023) 42:7
using BMI. BMI is less accurate than other methods
such as waist–hip ratio and skinfold thickness methods
to assess the type of overweight/obesity. Second, data
on maternal overweight/obesity such as dietary intake,
physical activity level, and health status were una-
vailable. ird, the study could not establish a causal
pathway of the association between explanatory and
dependent variables due to the cross-sectional nature
of the data. Fourth, because some of the independent
variables were self-reported, there may have been some
recall and social desirability bias, which is beyond the
control of the current study. Finally, considering the
skewed distribution of the DBM data findings should
be interpreted with caution.
Conclusion
Our study findings show a low prevalence of double
burden of malnutrition among mother–child pairs in
Ethiopia. Mothers with short and very short stature
were more likely to suffer from the double burden of
malnutrition. is link between short maternal height
and DBM may imply that high-risk mothers (those
who are short or very short in stature) should be pri-
oritized for sufficient nutritious food supply and opti-
mal nutrition to break the vicious cycle of malnutrition
that exists under the same roof. Again, existing nutri-
tion interventions must make significant and concerted
efforts to combat the growing concern of DBM in
Ethiopia.
Abbreviations
AOR Adjusted odds ratio
ANC Antenatal care visits
BMI Body mass index
CI Confidence interval
DBM Double burden of malnutrition
EDHS Ethiopian Demographic and Health Surveys
WHO World Health Organization
Acknowledgements
We would like to thank the Measure DHS Program for providing the DHS
datasets.
Author contributions
BS contributed to conceptualization, formal analysis, investigation, methodol-
ogy, project administration, and writing—original draft. LM contributed to
visualization, validation, and writing—review and editing. KEA contributed
to supervision, visualization, validation, and writing—review and editing. All
authors have read and approved the final manuscript.
Funding
No organization funded this research.
Availability of data and materials
The datasets analyzed during the current study are available on the Measure
DHS Web site https:// dhspr ogram. com after formal online registration and
submission of the project title and detailed project description.
Declarations
Ethics approval and consent to participate
The data were obtained via online registration to measure the DHS program
and downloaded after the purpose of the analysis was communicated and
approved. An approval letter for the use of the EDHS dataset was gained from
MEASURE DHS. Because this study was based on secondary analysis of pub-
licly available data with no personal identifier, no ethical considerations were
needed before undertaking it. All methods were carried out in accordance
with relevant guidelines and regulations.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
1 Department of Public Health, Madda Walabu University Goba Referral Hospi-
tal, Bale-Goba, Ethiopia. 2 Centre for Public Health Research, Equity and Human
Flourishing, Torrens University Australia, Adelaide Campus, Adelaide, SA 5000,
Australia. 3 School of Health Sciences, Western Sydney University, Locked Bag
1797, Penrith, NSW 2751, Australia. 4 School of Medicine, Translational Health
Research Institute, Western Sydney University, Campbelltown Campus, Penrith,
NSW 2571, Australia. 5 African Vision Research Institute, University of KwaZulu-
Natal, Durban 4041, South Africa.
Received: 22 December 2022 Accepted: 20 January 2023
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