![Inequality in stunting by regions (A) and grouped regions (B) (as in [55])](https://www.researchgate.net/publication/5801426/figure/fig1/AS:267328738820104@1440747546487/nequality-in-stunting-by-regions-A-and-grouped-regions-B-as-in-55_Q320.jpg)
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International Journal for Equity in
Health
Open Access
Research
Malnutrition and the disproportional burden on the poor: the case
of Ghana
Ellen Van de Poel*1,2, Ahmad Reza Hosseinpoor3, Caroline Jehu-Appiah4,
Jeanette Vega3 and Niko Speybroeck5
Address: 1Department of Applied Economics, Erasmus University Rotterdam, Burg. Oudlaan 50, 3000 DR Rotterdam, The Netherlands, 2The
Faculty of Economics and Commerce, The University of Melbourne, Victoria 3010, Australia, 3Equity, Poverty and Social Determinants of Health,
Evidence and Information for Policy, World Health Organization, Avenue Appia 20, CH - 1211 Geneva 27, Switzerland, 4Policy Planning
Monitoring and Evaluation Division, Ghana Health Service, Private Mail Bag, Ministries, Accra, Ghana and 5Institute of Tropical Medicine,
Nationalestraat 155, 2000 Antwerp, Belgium
Email: Ellen Van de Poel* - vandepoel@few.eur.nl; Ahmad Reza Hosseinpoor - hosseinpoora@who.int; Caroline Jehu-
Appiah - carojehu@yahoo.co.uk; Jeanette Vega - vegaj@who.int; Niko Speybroeck - nspeybroeck@itg.be
* Corresponding author
Abstract
Background: Malnutrition is a major public health and development concern in the developing
world and in poor communities within these regions. Understanding the nature and determinants
of socioeconomic inequality in malnutrition is essential in contemplating the health of populations
in developing countries and in targeting resources appropriately to raise the health of the poor and
most vulnerable groups.
Methods: This paper uses a concentration index to summarize inequality in children's height-for-
age z-scores in Ghana across the entire socioeconomic distribution and decomposes this inequality
into different contributing factors. Data is used from the Ghana 2003 Demographic and Health
Survey.
Results: The results show that malnutrition is related to poverty, maternal education, health care
and family planning and regional characteristics. Socioeconomic inequality in malnutrition is mainly
associated with poverty, health care use and regional disparities. Although average malnutrition is
higher using the new growth standards recently released by the World Health Organization,
socioeconomic inequality and the associated factors are robust to the change of reference
population.
Conclusion: Child malnutrition in Ghana is a multisectoral problem. The factors associated with
average malnutrition rates are not necessarily the same as those associated with socioeconomic
inequality in malnutrition.
Background
In the developing world, an estimated 230 million (39%)
children under the age of five are chronically malnour-
ished and about 54% of deaths among children younger
than 5 are associated with malnutrition [1]. Malnutrition
is a major public health and development concern with
important health and socioeconomic consequences. In
Sub-Saharan Africa, the prevalence of malnutrition
Published: 28 November 2007
International Journal for Equity in Health 2007, 6:21 doi:10.1186/1475-9276-6-21
Received: 21 August 2007
Accepted: 28 November 2007
This article is available from: http://www.equityhealthj.com/content/6/1/21
© 2007 Van de Poel et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
International Journal for Equity in Health 2007, 6:21 http://www.equityhealthj.com/content/6/1/21
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among the group of under-fives is estimated at 41% [1]. It
is the only region in the world where the number of child
deaths is increasing and in which food insecurity and
absolute poverty are expected to increase [2-4]. Malnutri-
tion in early childhood is associated with significant func-
tional impairment in adult life, reduced work capacity
and decreasing economic productivity [5-10]. Children
who are malnourished not only tend to have increased
morbidity and mortality but are also more prone to suffer
from delayed mental development, poor school perform-
ance and reduced intellectual achievement [6-8].
Chronic malnutrition is usually measured in terms of
growth retardation. It is widely accepted that children
across the world have much the same growth potential, at
least to seven years of age. Environmental factors, dis-
eases, inadequate diet, and the handicaps of poverty
appear to be far more important than genetic predisposi-
tion in producing deviations from the reference. These
conditions, in turn, are closely linked to overall standards
of living and the ability of populations to meet their basic
needs. Therefore, the assessment of growth not only serves
as one of the best global indicators of children's nutri-
tional status, but also provides an indirect measurement
of the quality of life of an entire population [11-13].
Large scale development programs such as the Millen-
nium Development Goals (MDGs) have also emphasized
the importance of the under-fives' nutritional status as
indicators for evaluating progress [14]. When aiming at
reducing childhood malnutrition, it is important not only
to consider averages, which can obscure large inequalities
across socioeconomic groups. Failure to tackle these ine-
qualities may act as a brake on making progress towards
achieving the MDGs and is a cause of social injustice
[15,16].
Ghana
Against this background, Ghana provides an interesting
case study. The country experienced remarkable gains in
health from the immediate post independence era. Life
expectancy improved over the years and the prevention of
a range of communicable diseases improved child sur-
vival and development. However in the last decade
despite increasing investments in health, Ghana has not
achieved target health outcomes. There has been no sig-
nificant change in Ghana's under-five and infant mortal-
ity rates between 1993 and 2003. In the last couple of
years, under-five mortality was actually slightly increas-
ing. Life expectancy has also fallen from 57 years in 2000
to 56 years in 2005 [17]. Ghana's Human Development
Index (HDI), a measure combing life expectancy, literacy,
education and standard of living, has been worsening too;
after improving from 0.444 in 1975 to 0.563 in 2001, the
HDI dropped to 0.520 in 2005 [15]. Since 1988, there has
been no definite trend in malnutrition (in terms of height-
for-age). Apparent gains between 1988 and 1998 were
reversed in 2003 [18]. Although the 2003 Ghana Demo-
graphic Health Survey (DHS) final report [17] recom-
mends caution when using data from the various DHS to
assess the trend in the nutritional status, it is noted that
there was a trend over the past five years of increased
stunting compared to a decrease of wasting and under-
weight. Further, there has been a trend of continued high
values of stunting in the North compared to the South
[17,19].
Malnutrition in Ghana has been most prevalent under the
form of Protein Energy Malnutrition (PEM), which causes
growth retardation and underweight. About 54% of all
deaths beyond early infancy were associated with PEM,
making this the single greatest cause of child mortality in
Ghana [20].
A paradigm shift in Ghanaian health policy has been tak-
ing place in 2006. The theme for the new health policy in
Ghana was 'Creating Wealth through Health". One of the
fundamental hypotheses of this policy was that improving
health and nutritional status of the population would
lead to improved productivity, economic development
and wealth creation [21]. Since this policy adopted an
approach that addressed the broader determinants of
health, it has thus generated interest in socio-economic
inequalities in health and malnutrition. It was further rec-
ognised that not paying attention to malnutrition ine-
qualities during the early years of life is likely to
perpetuate inequality and ill health in future generations
and thus defeat the aims of the new health policy.
From the existing evidence it is clear that childhood mal-
nutrition is associated with a number of socioeconomic
and environmental characteristics such as poverty, par-
ents' education/occupation, sanitation, rural/urban resi-
dence and access to health care services. Also
demographic factors such as the child's age and sex, birth
interval and mother's age at birth have been linked with
malnutrition [4,5,22-26]. Previous studies have also
drawn attention to the disproportional burden of malnu-
trition among children from poor households [27-31].
However, much less is known on which factors lie behind
this disproportional burden. It is important to note that
the most important determinants of malnutrition are not
necessarily also the most important determinants of soci-
oeconomic inequality in malnutrition. [31] shows that
the poorest-to-richest odds-ratio of stunting is almost
halved by controlling for household and child character-
istics using Ghanaian data. However, it is not clear how
much each of these characteristics is contributing to this
reduction. Understanding the nature and determinants of
socioeconomic inequality in malnutrition is essential in
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contemplating the health of populations in developing
countries and in targeting resources appropriately to raise
the health of the poor and most vulnerable groups. This
paper employs a concentration index to summarize ine-
quality across the entire socioeconomic distribution
rather than simply comparing extremes as in ratio meas-
ures. The concentration index is decomposed using the
framework suggested by [32], allowing to identify the fac-
tors that are associated with socioeconomic inequality in
malnutrition. This decomposition takes into account that
both the association of a determinant with malnutrition
as well as its distribution across socioeconomic groups
play a role in the extent to which it is contributing to soci-
oeconomic inequality in malnutrition. The usefulness of
this approach has already been demonstrated on Euro-
pean data, but has known limited applications on devel-
oping countries.
Further, this paper contributes to the literature by deliver-
ing evidence on the determinants of malnutrition and
socioeconomic inequality in Ghana using the new child
growth standards population that has recently been
released by the World Health Organization (WHO) [33].
This reference population includes children from Brazil,
Ghana, India, Norway, Oman and the US. The new stand-
ards adopt a fundamentally prescriptive approach
designed to describe how all children should grow rather
than merely describing how children grew in a single ref-
erence population at a specified time [34]. For example,
the new reference population includes only children from
study sites where at least 20% of women are willing to fol-
low breastfeeding recommendations. To our knowledge
this is the first study presenting estimates of malnutrition
in Ghana based upon these new standards. To check sen-
sitivity of the results to this change in reference group, the
analysis is also done using the US National Center for
Health Statistics (NCHS) reference population [35].
The results are useful from a policy perspective as they can
be used in setting policies to reduce malnutrition and the
excessive burden on the poor. The results of this study are
particularly relevant for Ghanaian policy makers, but can
also be generalized to other settings in the sense that they
show that malnutrition is associated with a broad range of
factors and that the factors related to average malnutrition
are not necessarily the same as those related to socioeco-
nomic inequality in malnutrition.
Methods
Measuring malnutrition
Nutritional status was measured by height-for-age z-
scores. An overview of other nutritional indices and why
height-for-age is the most suited for this kind of analysis
is provided in [36]. A height-for-age z-score is the differ-
ence between the height of a child and the median height
of a child of the same age and sex in a well-nourished ref-
erence population divided by the standard deviation in
the reference population. The new WHO child growth
population is used as reference population [33]. To con-
struct height-for-age z-scores based upon these standards,
we used the software available on the WHO website [37].
To check sensitivity of the results to this change in refer-
ence group, the analysis is also done by using the US
National Center for Health Statistics (NCHS) reference
population [35].
Generally, children whose height-for-age z-score is below
minus two standard deviations of the median of the refer-
ence population are considered chronically malnourished
or stunted. In the regression models, the negative of the z-
score is used as dependent variable (y). This facilitates
interpretation since it has a positive mean and is increas-
ing in malnutrition [32]. For the purpose of our analysis,
using the z-score instead of a binary or ordinal variable
indicating whether the child is (moderately/severely)
stunted is preferred as it facilitates the interpretation of
coefficients and the decomposition of socioeconomic ine-
quality. However, binary indicators of stunting are also
used in the descriptive analysis and to position Ghana
within a set of other Sub-Saharan African countries.
The concentration index as a measure of socioeconomic
inequality
Assume yi is the negative of the height-for-age z-score of
child i. The concentration index (C) of y results from a
concentration curve, which plots the cumulative propor-
tion of children, ranked by socioeconomic status, against
the cumulative proportion of y. The concentration curve
lies above the diagonal if y is larger among the poorer chil-
dren and vice versa. The further the curve lies from the
diagonal, the higher the socioeconomic inequality in
nutritional status. A concentration index is a measure of
this inequality and is defined as twice the area between
the concentration curve and the diagonal. If children with
low socioeconomic status suffer more malnutrition than
their better off peers the concentration index will be neg-
ative [38]. It should be noted that the concentration index
is not bounded within the range of [-1,1] if the health var-
iable of interest takes negative, as well as positive values.
Since children with a negative y are better off than chil-
dren in the reference population, they cannot be consid-
ered malnourished. Therefore their z-score is changed into
zero, such that the z-scores are restricted to positive values
with zero indicating no malnutrition and higher z-scores
indicating more severe malnutrition.
Further, the bounds of the concentration index depend
upon the mean of the indicator when applied to binary
indicators, such as stunting [39]. This would impede
cross-country comparisons due to substantial differences
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in means across countries. To avoid this problem, we used
an alternative but related concentration index that was
recently introduced by [40] and does not suffer from
mean dependence, when comparing Ghana with other
Sub-Saharan African countries.
Decomposition of socioeconomic inequality
More formally, a concentration index of y can be written
as [38]:
where yi refers to the height-for-age of the i-th individual
and Ri is its respective fractional rank in the socioeco-
nomic distribution. As will be discussed further in the fol-
lowing section, the present paper uses a continuous
wealth variable, developed by principal component anal-
ysis, as a measure of socioeconomic status [see e.g. [41]].
If yi is linearly modelled
[32] showed that the concentration index of height-for-
age can be decomposed into inequality in the determi-
nants of height-for-age as follows:
where μ is the mean of y, is the mean of xk, Ck is the
concentration index of xk (with respect to socioeconomic
status) and GC
ε
is the generalized concentration index of
the residuals. The latter term reflects the socioeconomic
inequality in height-for-age that is left unexplained by the
model and is calculated as .
As the DHS data have a hierarchical structure, with chil-
dren nested in households and households nested within
communities, we have also considered using multilevel
models to estimate the associations of variables with
childhood malnutrition (see e.g. [42]). Allowing for ran-
dom effects on the household and/or community level
yielded coefficients that were similar to the ones from OLS
regression corrected for clustering. Because of this similar-
ity and because the use of multilevel models would com-
plicate the decomposition of socioeconomic inequality in
malnutrition, the remainder is based on results from lin-
ear regression corrected for clustering on the community
level.
All estimation takes account of sample weights (provided
with the DHS data). Statistical inference on the decompo-
sition results is obtained through bootstrapping with
3000 replications. The bootstrap procedure takes into
account the dependence of observations within clusters.
Data
Data is used from the 2003 Ghana Demographic Health
Survey (DHS) and are restricted to children under the age
of 5. Anthropometric measures are missing for 12.3% of
children in this age group. The final sample contains
information on 3061 children. We did examine possible
selection problems due to the high proportion of missing
observations. A logit model explaining the selection in the
sample and a Heckman sample selection model (using
different exclusion restrictions) were used to check for this
[43]. Both tests did not reveal large sample selection prob-
lems, and coefficients in the Heckman model were very
similar to those in the model presented here.
The nutritional status of a child is specified to be a linear
function of child-level characteristics such as age, sex,
duration of breastfeeding, size at birth; maternal charac-
teristics such as education, mother's age at birth, birth
interval, marital status, use of health services, occupation
and finally household-level characteristics such as wealth,
type of toilet facility, access to safe water, number of
under-five children in the household, region and urbani-
zation. We preferred not to include information on the
type of toilet and water source into the wealth indicator,
as these variables can be expected to have a direct relation
with children's growth apart from being correlated with
household socioeconomic status [44].
The explanatory variables are described in the last column
of Table 1. All have well documented relevance in the lit-
erature [5,22-26,31,32,45,46].
No information on mother's nutritional status was
included in the set of explanatory variables. Since about
10% of women in the dataset were pregnant at the time of
interview, their BMI did not provide an accurate measure
of their nutritional status. Furthermore, BMI reflects cur-
rent nutritional status and may not be relevant for chil-
dren born 5 years prior to the interview. Inclusion of
mother's height-for-age had no significant effect on
results.
C
yR
y
ii
i
n
i
i
n
=−
=
=
∑
∑
2
1
1
1
yx
ikiki
k
K
=+ +
=
∑
αβε
1
,
CkxkCGC
k
K
k
=⎛
⎝
⎜⎞
⎠
⎟+
=
∑
β
με
μ
1
xk
GC R
nii
i
n
ε
ε
=
=
∑
2
1
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Table 1: Mean, standard deviation and description of all variables
Variable Mean SD Description
Stunting (WHO) 0.36 0.48 Height-for-age z-score<-2SD of WHO population (1-0)
Z-score (WHO) 1.58 1.27 Height for age z-score (based upon WHO)
Stunting (NCHS) 0.29 0.45 Height-for-age z-score<-2SD of NCHS population (1-0)
Z-score (NCHS) 1.41 1.17 Height for age z-score (based upon NCHS)
Breastfeeding 16.98 8.34 Duration of breastfeeding (in months)
Age of child
≤ 6 months 0.12 0.33 Age of child split into 3 categories: ≤ 6 months; 6–12 months, >12 months
6–12 months 0.12 0.32
> 12 months 0.76 0.43
Size of child
Size large 0.41 0.49 Size of child at birth in 4 categories: very large, large, normal, small, very small
Size normal 0.41 0.49
Size small 0.12 0.32
Size very small 0.06 0.24
Sex of child 0.50 0.50 Sex of child: male(1), female (0)
Region
Upper 0.09 0.29 region of residence: Upper (Upper East and Upper West), Middle (Ashanti and
Brong Ahafo), South (Western, Central, Volta and Eastern), Accra, Northern [55]
Middle 0.30 0.46
South 0.36 0.48
Accra 0.11 0.31
Northern 0.14 0.34
Urban 0.33 0.47 Urban location (1), rural location (0)
Wealth
Poor 0.39 0.49 Wealth groups (poor) based upon principal component analysis. The wealth
indicator is estimated on household level and combines the following assets:
electricity, radio, TV, fridge, bike, motor, car, phone and the type of the flooring
material [60].
Middle 0.32 0.47
Rich 0.29 0.45
Toilet 0.70 0.46 Having a toilet (flush toilet, traditional pit toilet, ventilated improved pit latrine) (1-0)
Water 0.61 0.49 Whether the household has access to safe water available (1-0). The following
sources of water supply were regarded as safe water: piped water (piped into
dwelling, piped into yard, plot, or public tap); water from protected well
Twoplus 0.59 0.49 Whether there are more than two under-fives in the household (1-0)
Riskintb 0.10 0.30 Whether there were less than 24 months between the child's birth and the birth of
the previous child (1-0)
Married 0.91 0.29 Whether the child's mother is married or living together (1-0)
Mother's education
No or incomplete 0.56 0.50 Mother's education level split into 3 categories: no or incomplete primary, primary
and incomplete secondary, secondary and higher
Primary 0.40 0.49
Secondary and higher 0.04 0.20
Health services index
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Results
Summary statistics
In the 2003 DHS data for Ghana, 36% of children under
the age of 5 are stunted. Stunting is defined as height-for-
age being below minus 2 SD from the median of the ref-
erence population. The concentration index for stunting
in children under the age of 5 was -0.12 (SD = 0.016). This
negative value implies that poor children had a higher
probability of being stunted than their better off peers.
Using the older NCHS reference study showed a lower
prevalence of stunting (29%) and slightly higher socioe-
conomic inequality (C = -0.15, SD = 0.019).
Figure 1 illustrates the strong socioeconomic inequality in
childhood stunting. The stunting rate among the poorest
60 percent was more than twice the rate of children in the
richest 20 percent.
Figure 2 shows a comparative picture of stunting and soci-
oeconomic inequality in stunting across the Sub-Saharan
African region. Stunting and socioeconomic variables are
calculated for each country on DHS data in exactly the
same way as is described for the Ghana DHS. Summary
statistics of all variables are shown in Table 1.
Determinants of malnutrition
The regression coefficients and their significance are
shown in the first column of Table 2. Note that the
dependent variable is increasing in malnutrition, such
that a negative coefficient should be interpreted as lower-
ing malnutrition.
Malnutrition increased with the child's age in a non-linear
way. Children who were very small at birth had a higher
probability to be stunted than children with normal size.
Male children were more prone to malnutrition than their
female peers. Long duration of breastfeeding is associated
with higher malnutrition.
With respect to maternal characteristics, the existence of a
short birth interval was significantly increasing malnutri-
tion. Children of women that accessed health services
more frequently were less prone to being malnourished.
Maternal occupation showed no clear effect. Maternal
education and household wealth showed a significant
association with childhood malnutrition. The presence of
two or more under-five children in the household was
negatively associated with the child's nutritional status.
Sanitation variables however had no significant associa-
tion on malnutrition. As compared to the Northern region
all regions were associated with lower malnutrition, espe-
Distribution of stunting across wealth quintilesFigure 1
Distribution of stunting across wealth quintiles.
0
10
20
30
40
50
Q1
Q2
Q3
Q4
Q5
% of stunted children
stunting rates based upon WHO stunting rates based upon NCHS
Healthlow 0.33 0.47 Use of health services (low, moderate, high) estimated by principal component
analysis. The indicator combines skilled birth attendance, antenatal care and
proportion of recommended vaccinations [45]. The age schedule from the Expanded
Program on Immunization set by the WHO was used: BCG at birth, DPT and Polio
at 2, 3 and 4 months and measles at 9 months.
Healthmod 0.32 0.46
Healthhigh 0.31 0.46
Mother's age at birth
<20 0.11 0.31 Mother's age at birth in years split into 3 categories: <20, 20–39, >39
20–39 0.81 0.39
>39 0.08 0.27
Mother's occupation
Prof, tech, man, cler,
sales, service
0.32 0.47 Professional, technical, managerial, clerical, sales, services; agriculture; manual; not
working
Agriculture 0.44 0.50
Manual 0.14 0.35
Not working 0.10 0.30
Observations 3061
Reference categories for categorical variables used in the regression model are in bold.
Table 1: Mean, standard deviation and description of all variables (Continued)
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cially the Accra region. The high regional disparities in
malnutrition are further illustrated in Figure 3. The four
most deprived regions in Ghana (Northern, Central,
Upper East and Western regions) exhibited the greatest
burden of malnutrition.
Decomposition of socioeconomic inequality in
malnutrition
Table 2 also shows the concentration index and the rela-
tive contributions of each determinant to socioeconomic
inequality in childhood malnutrition. For the ease of
interpretation, the last column shows the grouped contri-
bution from the categorical variables. A negative contribu-
tion to socioeconomic inequality implies that the
respective variable is lowering socioeconomic inequality
and vice versa. A variable can contribute to socioeconomic
inequality in malnutrition both through its association
with malnutrition and through its unequal distribution
across wealth groups. The extent to which each of the
explanatory variables is unequally distributed across
wealth is reflected by its C value. A negative C means that
the determinant is more prevalent among poorer house-
holds.
Wealth accounted for the major part (31%) of socioeco-
nomic inequality. This part of socioeconomic inequality
reflects the direct contribution of wealth. The remainder is
the wealth-related inequality in malnutrition through
other factors. Important contributors were regional varia-
bles (23%) and the use of health care services (18%). The
age of the child was contributing negatively to socioeco-
nomic inequality (-8%). This means that the combined
effect of its coefficient and its distribution by wealth was
lowering socioeconomic inequality in malnutrition.
Older children were more likely to be stunted and were
more prevalent in higher wealth quintiles. The latter is
reflected by the positive and significant C of the variable
age>12 months The contribution of the error term only
amounted to about 6%, meaning that the decomposition
model functioned well in explaining socioeconomic ine-
quality in malnutrition.
Using the older NCHS reference population gave very
similar regression and decomposition results are therefore
not discussed (results are available upon request.).
Discussion
Relative to other Sub-Saharan countries, Ghana appeared
to have a rather low level of average stunting, combined
with relatively high socioeconomic inequality in stunting.
The use of the new WHO child growth standards yielded
a higher average stunting rate as compared to the older
NCHS reference group. [47] found the same for Bangla-
desh, Dominican Republic and a pooled sample of North
American and European children. However, the variables
associated with malnutrition and socioeconomic inequal-
ity were very robust to the change of the reference popula-
tion.
Average stunting versus socioeconomic inequality in stunting in under-five children in Sub-Saharan AfricaFigure 2
Average stunting versus socioeconomic inequality in stunting in under-five children in Sub-Saharan Africa. Data from recent
Demographic Health surveys. Stunting is measured using the WHO child growth standards. Concentration index as suggested
by [40] is used since it is invariant to the mean of the binary variable.
Togo Tanzania
Zimba bw e
Zambia
Uga n d a
Rwanda
Nigeria
Niger
Namibia
Mozambique
Maurita nia
Mali Malawi
Madagascar
Ken y a
Guinea
Gha na
Gabon
Ethiopia
Cote d'Ivoire Comoros
Chad
CAR
Ben i n
-0.3
-0.25
-0.2
-0.15
-0.1
-0.05
0
0 0.1 0.2 0.3 0.4 0.5 0.6
aver age s tunti ng
C of stunting
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Determinants of malnutrition
Malnutrition in Ghanaian children rises with the age of
the child, which is confirmed by other studies [5,25,32].
The higher prevalence of malnutrition among boys as
compared to girls, and the negative association of long
breastfeeding have also been established in the literature
[5,22,32,45]. Long duration of breastfeeding may be asso-
ciated with higher malnutrition because it reflects lack of
resources to provide children with adequate nutrition
[31]. It is also possible that children who are breastfed for
a long time are more reluctant to eat other foods, as was
found by [22] in their study on a cohort of Ghanaian chil-
dren.
Short birth intervals and the presence of two or more
under-five children in the household, affected childhood
growth negatively by placing a heavy burden on the
mother's reproductive and nutritional resources, and by
increasing competition for the scarce resources within the
household [22]. Children of younger mothers could be
more prone to malnutrition because of physiological
immaturity and social and psychological stress that come
with child bearing at young age [48].
Maternal education was significantly lowering childhood
malnutrition. This may reflect education generating the
necessary income to purchase food. However, although
education is often suggested to be a measure of social sta-
Inequality in stunting by regions (A) and grouped regions (B) (as in [55])Figure 3
Inequality in stunting by regions (A) and grouped regions (B)
(as in [55]).
0
10
20
30
40
50
60
Northern
Western
Central
Accra
Volta
Eastern
Ashanti
Ahafo
Upper West
Upper East
% of stunted children
stunting rate s based upon W HO stunting ra tes base d upon NCHS
0
10
20
30
40
50
Northern
Middle
Southern
Accra
% of stunted children
stunting rat es based upon W HO stunting ra tes base d upon NCHS
A
B
A
B
Table 2: Regression and decomposition results: coefficient,
concentration index (C) and proportional contribution
Variables Coefficient C Contribution
(%)
Contribution
(%)
Breastfeeding 0.01 -0.0042 0.54 0.54
Age of child -8.14
6–12 months 0.22 0.0049 -0.10
> 12 months 0.86 0.0154 -8.04
Size of child 2.01
Size large -0.12 0.0170 0.65
Size small 0.18 -0.0500 0.82
Size very small 0.26 -0.0401 0.54
Sex of child 0.23 -0.0101 0.92 0.92
Region 23.07
Upper -0.59 -0.2123 -8.29
Middle -0.38 0.1169 10.34
South -0.52 -0.0425 -6.68
Accra -0.73 0.4390 27.70
Urban -0.11 0.3153 8.95 8.95
Wealth 30.85
Middle -0.04 0.1055 1.13
Rich -0.18 0.7120 29.71
Toilet -0.10 0.1159 6.71 6.71
Water 0.02 0.0690 -0.72 -0.72
Twoplus 0.11 -0.0469 2.41 2.41
Riskintb 0.19 0.0440 -0.66 -0.66
Married -0.03 0.0180 0.35 0.35
Mother's education 5.51
No or incomplete 0.33 -0.1578 22.99
Primary 0.36 0.1549 -17.48
Health services index 18.32
Healthmod -0.02 -0.0525 -0.20
Healthhigh -0.32 0.2204 18.52
Mother's age at birth 1.29
<20 0.13 -0.1133 1.26
>39 0.00 -0.1035 0.03
Mother's occupation 2.90
Prof, tech, man,
cler, sales, service
-0.13 0.2194 7.40
Agriculture -0.07 -0.1884 -4.90
Manual -0.07 0.0505 0.40
Constant 1.03
Error -0.0045 5.70 5.70
Total 100.00 100.00
The dependent variable in the regression is the (negative) height-for-age z-score
(based upon the WHO reference population). Number of observations = 3061, C
of dependent variable = -0.079. Bold numbers indicate significance at the 10% level
(based upon bootstrapped standard errors).
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tus, the coefficient stayed significant after controlling for
household wealth and living conditions. A high level of
maternal education could also lower childhood malnutri-
tion through other pathways such as increased awareness
of healthy behaviour, sanitation practices and a more
equitable sharing of household resources in favour of the
children [4,5,49].
Sanitation in terms of having a toilet and access to safe
water did not significantly affect malnutrition. [26] also
reported this result, but they did find a significant associ-
ation between sanitation and wasting (which reflects cur-
rent nutritional status). This might suggest that good
sanitation can avoid episodes of diarrhoea and hereby
affect current nutritional status, while it may not be suffi-
cient for long term child growth.
The higher levels of malnutrition of the population living
in the northern regions of Ghana have already been
observed more than a decade ago [23]. This regional pat-
tern reflects ecological constraints, worse general living
conditions and access to public facilities in the Northern
regions. In addition, the persistence of this regional ine-
quality can point to an intergenerational effect of malnu-
trition. Since women who were malnourished as children
are more likely to give birth to low-birth-weight children,
past prevalence of child malnutrition is likely to have an
effect on current prevalence.
Decomposition of socioeconomic inequality in
malnutrition
The high socioeconomic inequality in childhood malnu-
trition is -apart from wealth itself-mainly associated with
regional characteristics and use of health care services.
Wealth was responsible for about one third of the socioe-
conomic inequality in malnutrition. This means that
poorer children were more likely to be malnourished,
mainly because of their poverty. The regional contribu-
tion results from the fact that poorer children are more
likely to live in regions with disadvantageous characteris-
tics. Given the strong regional associations with malnutri-
tion, after controlling for a broad range of socioeconomic
and demographic covariates, there must be other impor-
tant regional aspects. The regional inequality in Ghana
originates from both geographical and historical reasons.
Much of the North is characterized by lower rainfall,
savannah vegetation, periods of severe drought and
remote and inaccessible location. Further, the colonial
dispensation ensured that northern Ghana was a labor
reserve for the southern mines and forest economy and
the post-colonial failed to break the established pattern
[19].
Health services use was also responsible for a substantial
proportion of socioeconomic inequality in malnutrition.
This derives from the combined effect of the positive asso-
ciations between health services use and childhood
growth and the unequal use across socioeconomic groups.
The reason for the lower health care use amongst the poor
may be due to several barriers including the cost of care,
cost of transportation and lower awareness on health pro-
moting behavior [50]. User fees were introduced in Ghana
in 1985 as a cost-sharing mechanism at all public health
facilities. To ensure access to health care services for the
poor and vulnerable the government introduced fee
exemptions. Then again in 2003, a new policy for exempt-
ing deliveries from user fees in the four most deprived
regions of the country, namely Central, Northern, Upper
East and Upper West regions were introduced. To further
bridge the inequality a key recommendation of the Ghana
Poverty Reduction Strategy [51] was to allocate 40% of the
non-wage recurrent budget to the deprived regions. How-
ever, experience to date indicates that Ghana has not been
able to implement an efficient exemption mechanism or
commit to the 40% budgetary allocation to achieve the
principal purpose. In addition to these financial hurdles,
poorer people are often also located further from health
centers. The ratios of population to nurses and doctors are
the highest the poorest regions of Ghana. For example the
ratio of population to doctors in the northern region is
1:81338 compared to the national average of 1:17733.
Trends show that since 1995 the Northern region has had
the lowest average number of outpatient visits per capita
in the country [52]. Also partly related to the use of health
services is the contribution of the number of under-fives
in the household. Poor women are more likely to have
more children and these, in turn, are therefore more likely
to be malnourished. The higher parity among poorer
women may be related to difficult access to or knowledge
on family planning services. The much lower use and
knowledge of modern contraception among poor women
is documented in the Ghana DHS 2003 final report [17].
The negative contribution of age comes from the com-
bined facts that older children are more likely to be mal-
nourished and at the same time more prevalent in the
richer wealth quintiles. The latter could be related to
higher child and infant mortality rates amongst poorer
households that cause the proportion of older children to
be lower among poor households as compared to richer
households.
Combining the results from the analysis on the determi-
nants of malnutrition and socioeconomic inequality
demonstrates that variables that are associated with aver-
age malnutrition are not necessarily also related to socio-
economic inequality. Although bio-demographic
variables such as a risky birth interval, size at birth, dura-
tion of breastfeeding and the sex of the child are quite
strongly associated with a child's nutritional status, they
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do not contribute to socioeconomic inequality in malnu-
trition. This is because of their relatively equal distribu-
tion across socioeconomic groups. Other variables such as
urban/rural location, having a toilet, access to clean water
and maternal occupation are very unequally distributed
across socioeconomic groups, but still do not contribute
to socioeconomic inequality in malnutrition because they
are not significantly associated with malnutrition. A third
group of variables such as regions, health care use and
wealth are both very strongly related to average and soci-
oeconomic inequality in malnutrition.
Considerations and limitations
There exist some limitations of this study. First, DHS only
collects information on the recent food consumption of
the youngest child under three years of age living with the
mother. Restricting the sample to these children would
substantially reduce the number of observations. How-
ever, the analysis was also conducted on this sub sample,
using food consumption as one of the determinants of
malnutrition (indices were created similar to [25,45]).
Since the regression and decomposition results did not
differ much, these are not presented in this paper (but are
available from the authors upon request). Second, one
has to bear in mind that, although commonly used, the
construction of an asset index to capture socioeconomic
status has its shortcomings and e.g. is sensitive to the
assets included [44]. However, in the absence of reliable
information on income or expenditure, the use of such an
asset index is generally a good alternative to distinguish
socioeconomic layers within a population [53]. Finally, it
is important to note that this paper is showing the factors
that are associated with malnutrition and socioeconomic
inequality in malnutrition and the magnitude of these
associations. These results are subject to the usual caveats
regarding the causal interpretation of cross-sectional
results. Focusing on child health avoids much of the direct
feedback of income and health that is usually present in
microeconomic studies. To gain some insight into the
severity of endogeneity problems we also did the analysis
excluding possible endogenous variables such as birth
interval, breastfeeding, the number of children in the
household and use of health care services. Again, wealth
and regional characteristics were contributing most to
socioeconomic inequality, followed by maternal educa-
tion. To avoid endogeneity of health care use, it would be
better to use data on proximity/availability of care. How-
ever, no such data were available in the 2003 Ghana DHS.
Another option would be to predict health care use, but
we were not able to find strong predictors for health care.
Conclusion and policy implications
The regression results show that malnutrition is associated
with a broad range of factors. However in Ghana it often
falls through the cracks since it has no institutional home.
Tackling malnutrition therefore calls for a shared vision
and should be viewed and addressed in a broader context
[54]. Therefore special attention needs to be given to pol-
icies aimed at reducing malnutrition based on the magni-
tude and nature of determinants of malnutrition, such as
poverty, education, health care and family planning serv-
ices and regional characteristics. Currently in Ghana, var-
ious interventions are being implemented to reduce both
PEM and micro nutrient deficiencies. These include the
Infant and Young Child Feeding Strategy (IYCF) and
Community Based Nutrition and Food Security project
among others. Notwithstanding the positive effects of
these programs, they address only the symptoms of mal-
nutrition and therefore are most likely not sufficient to
have a sustained impact in the long term as they do not
deal with a lot of the root causes of malnutrition.
The results also suggest that factors strongly associated
with average malnutrition are not necessarily also contrib-
uting to socioeconomic inequality in malnutrition. The
distinction between these groups of variables can be quite
important, as it suggests that policies trying to reduce aver-
age malnutrition rates can be different from those aiming
at lowering socioeconomic inequality in malnutrition. If
equity goals are to be achieved, health policies in Ghana
should further be directed at strategies/interventions to
reduce poverty and to improve the use of health care and
family planning services among the poorer population
groups. Furthermore, regional disparities should further
be tackled to narrow the gap in malnutrition between the
poor and the rich. A starting point could be for policy
makers to include under-five malnutrition differentials to
set criteria to guide resource allocation to regions. Moreo-
ver, the strong regional contributions to socioeconomic
inequality, even after controlling for other factors such as
household wealth and education, bring forward the issue
of geographical targeting. Further targeting public pro-
grams towards the central and northern regions would
substantially reduce socioeconomic inequality in malnu-
trition and is administratively easier than targeting the
poor. The latter argument is relevant for Ghana, where
pro-poor policies (redistribution schemes and exemption
policies) are not having the aimed effect because of prob-
lems in identifying the poor [55,56]. Geographic targeting
reduces leakage of program benefits to the non-needy
compared to untargeted programs, although under cover-
age of the truly needy can increase. "Fine-tuning" the tar-
geting by basing it on smaller geographic units increases
efficiency, but in some circumstances may be costly and
politically unacceptable [57].
With respect to Ghana, regional averages should be inter-
preted with caution as there is large heterogeneity
between districts in each region and indeed among socio-
economic groups within districts. In this case, polices
International Journal for Equity in Health 2007, 6:21 http://www.equityhealthj.com/content/6/1/21
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aimed at reducing child malnutrition based on regional
averages may lead to under coverage of those in need. [58]
exposes some important limitations of geographic target-
ing if used to place poverty-alleviation or nutrition inter-
ventions within cities. Using data from Abidjan (Cote
d'Ivoire) and Accra (Ghana), they found significant clus-
tering in housing conditions; however they did not find
any sign of geographic clustering of nutritional status in
either city. This implies that geographic targeting of nutri-
tion interventions in these and similar cities has impor-
tant limitations. Geographic targeting would probably
lead to a significant under coverage of the truly needy and,
unless accompanied by additional targeting mechanisms,
would also result in significant leakage to non-needy pop-
ulations. Nonetheless, there is a need for additional
research to further decompose regional malnutrition ine-
qualities to generate valuable information for policy mak-
ing decisions. The Ghana Growth and Poverty Reduction
Strategy for 2006 – 2009 [59] states that one of the strate-
gies to be implemented is developing and implementing
high impact yielding strategies for malnutrition. This
would mean targeting areas at the greatest risks of malnu-
trition, replicate best practices and expand coverage. This
then should result in decreasing malnutrition rates among
children particularly in rural areas and northern Ghana.
Authors' contributions
EVDP was responsible for the study design, analysis, inter-
pretation of the data and writing of the paper. ARH and
NS contributed to formulating the study design, interpret-
ing the data and revising the manuscript. CJA contributed
to the writing of the paper and revising the manuscript. JV
provided guidance to the work and commented on the
manuscript. All authors approved the final version of the
paper.
Acknowledgements
Many thanks to Tom Van Ourti, Owen O'Donnell, Eddy van Doorslaer and
participants at the UNU-WIDER conference on Advancing Health Equity for
useful comments. Ellen Van de Poel acknowledges the University of Ant-
werp and the World Health Organization for support and funding.
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