RESEARCH ARTICLE Open Access
An analysis of socio-demographic patterns in
child malnutrition trends using Ghana
demographic and health survey data in the
malnutrition. This paper shows the importance of disaggregated analyses of national child malnutrition data, to
Amugsi et al. BMC Public Health 2013, 13:960
http://www.biomedcentral.com/1471-2458/13/960University of Bergen, Christiesgt. 13, 5020 Bergen, Norway
Full list of author information is available at the end of the articleunmask underlying geographic and socio-demographic differences.
Keywords: Malnutrition, Children under-five, Socio-demographic factors, Ghana
* Correspondence: damugsi2002@yahoo.com
1Department of Health Promotion and Development, Faculty of Psychology,national trend masks important deviations in certain socioperiod 1993–2008
Dickson A Amugsi1*, Maurice B Mittelmark1 and Anna Lartey2
Abstract
Background: A small but growing body of research indicates that progress in reducing child malnutrition is
substantially uneven from place to place, even down to the district level within countries. Yet child malnutrition
prevalence and trend estimates available for public health planning are mostly available only at the level of global
regions and/or at country level. To support carefully targeted intervention to reduce child malnutrition, public
health planners and policy-makers require access to more refined prevalence data and trend analyses than are
presently available. Responding to this need in Ghana, this report presents trends in child malnutrition prevalence
in socio-demographic groups within the country’s geographic regions.
Methods: The study uses the Ghana Demographic and Health Surveys (GDHS) data. The GDHS are nationally
representative cross-sectional surveys that have been carried out in many developing countries. These surveys
constitute one of the richest sources of information currently available to examine time trends in child malnutrition.
Data from four surveys were used for the analysis: 1993, 1998, 2003 and 2008.
Results: The results show statistically significant declining trends at the national level for stunting (F (1, 7204) = 7.89,
p ≤ .005), underweight (F (1, 7441) = 44.87, p ≤ .001) and wasting (F (1, 7130) = 6.19, p ≤ .013). However, analyses of
the sex-specific trends revealed that the declining trends in stunting and wasting were significant among males but
not among females. In contrast to the national trend, there were significantly increasing trends in stunting for
males (F (1, 2004) = 3.92, p ≤ .048) and females (F (1, 2004) = 4.34, p ≤ .037) whose mothers had higher than primary
education, while the trends decreased significantly for males and females whose mothers had no education.
Conclusions: At the national level in Ghana, child malnutrition is significantly declining. However, the aggregate
-demographic segments, including worsening levels of© 2013 Amugsi 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.
Amugsi et al. BMC Public Health 2013, 13:960 Page 2 of 16
http://www.biomedcentral.com/1471-2458/13/960Background
Child malnutrition is a long-standing and continuing pub-
lic health problem in many regions of the world, despite
global-level progress in the reduction of this problem du-
ring the past two decades [1]. Globally, it is estimated that
childhood stunting (short stature for age) reduced from
34% to 27% and underweight from 27% to 22%, between
1990 and 2000 [1] and stunting is predicted to reduce to
22% by the next decade [2]. However, evidence shows that
global estimates cannot be used to monitor progress at
the regional level [3]. In Africa, the prevalence of stunting
declined marginally from 40.5% in 1980 to 35.2% in 2000
[3], and between 1990 and 2010 the prevalence of child-
hood stunting in Africa stagnated at about 40% (2). If this
trend continues, we cannot expect significant improve-
ment in the next decade. Global estimates are not good
indicators of regional trends, and it can be misleading to
use regional-level data to estimate the magnitude and
trend in malnutrition at the sub-regional level. During the
same period when stunting decreased in North Africa, it
actually increased in Eastern Africa, while Western Africa
showed very little change [3].
The lesson about the limits to generalizability of large
area data to smaller areas is pertinent also to intra-national
demographic divisions such as urban/rural, Regions, States
and Districts. In 2008 World Health Organization (WHO)
estimates of the prevalence of stunting in Ghana, the
urban rate was 22% while the rural rate was 33%; the Re-
gion with the lowest rate was Greater Accra with 17%
while the Eastern region had the highest rate of 39%, more
than two-fold difference [4].
Aside from geographic variation, child malnutrition
prevalence varies substantially by socio-demographic fac-
tors such as the child’s age and sex [5-8]. Therefore, in
order to study trends in malnutrition at a level useful to
public health planning, analyses need to be stratified by
defined socio-demographic segments; overall trends may
mask important departures from the general trend.
It is also vital that malnutrition be defined consistently
over the time that a trend analysis is undertaken, and
this has been challenging in the area of child malnutri-
tion. The National Centre for Health Statistics (NCHS)
international growth reference, that had been in use by
WHO since 1978, were changed in 2006. Therefore, the
published estimates before and after 2006 are not com-
parable, except for studies that have used the old stan-
dards also in the analysis of data generated since 2006.
This strategy results in consistency [9,10].
Health ministries, as well as governmental and non-
governmental health and development agencies, need ac-
cess to new analyses of child malnutrition and long-term
trends, that use the new WHO growth standards exclu-
sively, and that provide disaggregated estimates for defined
socio-demographic segments of the under-five population.Turning to the socio-demographic factors that influ-
ence child under-nutrition, there is a vast theoretical
and empirical literature advancing hypotheses and data
on the link between socio-demographic factors and
health generally and child health in particular [11-13]; a
review of that literature is well beyond the scope of this
paper. However, below is a detail description of various
factors that might influence the nutritional status of
children under five years.
Child age and gender are of such fundamental importance
to the study of malnutrition time trends that they should
not be ignored. Normal child growth and malnutrition are
defined by WHO growth standards separately for boys and
girls and for age bands separating major phases of develop-
ment such as pre- and post-weaning. Yet many, if not most
published studies, tend to group boys and girls and/or fail
to undertake age-specific analyses. When age- and gender-
specific analyses are undertaken, they show consistent differ-
ences in malnutrition prevalence that would have been
masked by less-differentiated analyses [5-8].
Furthermore, a consistent finding in child malnutrition
studies is that children who reside in rural areas have
higher rates of stunting and underweight than those in
urban areas [6-8,14,15], while the pattern for wasting is
somewhat inconsistent [16]. Even when controlling for
poverty level, malnutrition prevalence is always lower in
urban areas [17] than in rural area. Aside from urban/
rural differences, significant intra-country regional vari-
ation in child malnutrition has been documented [6,7]. In
Ghana, the regions with the greatest burden of child mal-
nutrition are those in the northern part of the country [5].
De Poel and colleagues [5] point out that the regional pat-
terns observed in Ghana reflect ecological constraints,
worse general living conditions, and poorer access to pub-
lic facilities in the Northern regions. There is also an
intergenerational aspect, with Northern women who suf-
fered from malnutrition when they were children being
more likely to give birth to children with low birth weight
[5,18], placing such children at risk of being malnourished
[19]. As for urban/rural trends, new analyses are needed
to track regional time trends in malnutrition.
One of the most robust findings in the public health
literature is that health is associated with socio-economic
status in a graded way. Increments in household mater-
ial standards (as measured for example by the Wealth
Index) are associated with increments in health [20], in-
cluding child nutritional status [8,21,22]. Children from
wealthy households are better off compared to children
from poor households. In Ghana for instance, children
in the poorest households are at more than twice the
risk of being stunted compared to those from the
richest households [23]. Hence, prevalence and trend
studies of child malnutrition should differentiate among
socio-economic status groups.
Amugsi et al. BMC Public Health 2013, 13:960 Page 3 of 16
http://www.biomedcentral.com/1471-2458/13/960Additionally, the level of maternal education is consist-
ently, although sometimes only weakly, associated with
child malnutrition [24-26].
As the time when an accounting of progress on develop-
ment goals for the Global South is coming due – especially
the Millennium Development Goals in 2015—planners
and policy makers need reliable and valid information on
child malnutrition trends that are sufficiently detailed in
their socio-demographic breakdown to illuminate where
goals are being met, and where challenges remain. The
needed data exist, gathered in four Ghana Health and
Demographic Surveys spanning 15 years from 1993 to
2008. However, these data require reanalysis, using current
WHO Child Growth Standards and providing breakdowns
by key socio-demographic segments.
The aims of this paper are: [1] to present findings on
national child malnutrition prevalence trends in Ghana
in the period 1993 to 2008; [2] to disaggregate the na-
tional level data to socio-demographic and geographic
sub-groups, and [3] to compare and contrast the na-
tional level findings on malnutrition with analysis based
on the sub-group.
Methods
Data sources
This study uses data from the Ghana Demographic and
Health Surveys [27]. The surveys were conducted in
1993, 1998, 2003 and 2008 by the Ghana Statistical
Service and Ghana Health Service, with technical and fi-
nancial support from ICF Macro through the USAID-
funded MEASURE DHS programme. The surveys were
designed to be representative at the national, regional
and rural–urban levels. A two-stage probabilistic sam-
pling design was used to select clusters (census districts)
at the first stage. The second stage involved the selection
of households from these clusters. All women and men
aged 15–49 in the selected households were eligible to
participate in the surveys. The household response rates
were 98.4% in 1993, 99.1% in 1998, 98.7% 2003, and
98.9% in 2008. The data were collected at two levels—
the household and individual levels. At the household
level, information was collected on household character-
istics such as source of drinking water, toilet facilities,
cooking fuel, and assets of the household. At the individ-
ual level, questionnaires were administered to women
aged 15–49 and men aged 15–59 to gather information
on individual characteristics and health behaviors, and
information on children in the household.
Variables
Nutritional Status of Children
Child nutritional status was assessed by height-for-age z-
scores, weight-for-height z-scores and weight-for-age z-
scores using the new WHO Child Growth Standards [28].A child was considered stunted, wasted or underweight if
their height-for-age, weight-for-height or weight-for-age
z-scores were further than −2 standard deviations from
the median of the reference sample used to construct the
WHO 2006 growth standards.
The DHS 2008 survey used the new WHO Child
Growth Standards [29], while the earlier DHS surveys
used the NCHS growth reference [30-32]. For the pur-
poses of cross-survey comparability, we calculated z-
scores using the new WHO Child Growth Standards,
using a syntax file provided by the WHO [33]. This syntax
file automatically flagged all biological implausible values.
Thus, height-for-age z-scores less than −6.0 and greater
than +6.0, weight-for-height z-scores less than −5.0 and
greater +5.0 and weight-for-age z-scores less than −6.0
and greater than +5.0 are excluded from our analysis.
Socio-demographic variables
The socio-demographic variables included child sex and
age, mother’s education, urban/rural residence, region of
residence and Wealth Index (composed using factor ana-
lysis to combine household-level information about hous-
ing quality and ownership/access to material goods).
Some of the variables were re-coded in order to attain
reasonable sample sizes, and also based on suggestions in
the literature. For maternal education, incomplete and
complete primary were recoded as “Primary”, and incom-
plete secondary, complete secondary and tertiary as “Some
high school or higher”. The region variable was recoded
into five categories—Upper East and West regions as
“Upper”, Ashanti and Brong Ahafo regions as “Middle”,
Western, Central, Volta and Eastern regions as “South”
while Greater Accra and Northern regions remained
“Accra” and “Northern” respectively [34].
Data analysis
We used SPSS for windows version 19.0 to perform the
data analysis. Using the definitions described above, chil-
dren were classified as stunted/not stunted, wasted/not
wasted and underweight/not underweight. All analyses
were stratified by sex. We used the Chi Square test for
homogeneity to calculate the confidence intervals for
prevalence estimates, which are reported in the Tables.
We used logistic regression to test the significant of trends
over time. The results of these tests are given in the text
only (and not in the Tables). A trend was considered sta-
tistically significant if the p-value was less than 0.05. Since
the DHS sampling design includes both under- and over-
sampling, all analyses were conducted with sample-
weighted data. The weights also accounted for non-
response. It is possible to use multi-level methods to ad-
just for cluster-level design effect. This should be done in
analyses that are sensitive to within-census district social
commonalities. We have not adjusted for the possible
design effect of cluster, due to the implausibility that cen-
sus district is an important source of dependency in the
child growth data. This strategy avoids over-adjustment of
the analyses.
Ethics
The DHS project sought and obtained the necessary eth-
ical approvals from ethics committees in Ghana before
the surveys were carried out. Informed consent was
obtained from study participants before they were
allowed to participate in the surveys. The survey data
sets used in this report were completely anonymous with
regard to participant identity.
in Tables 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 and
17 are also tests of homogeneity). Tests for linear trends
were undertaken using logistic regression; all F statistics
and associated probabilities reported in this text were cal-
culated using logistic regression. The declining trend in
total stunting was statistically significant (F (1, 7204) =
7.89, p ≤ .005). This was also the case for underweight (F
(1, 7441) = 44.87, p ≤ .001) and wasting (F (1, 7130) = 6.19,
p ≤ .013). However, the analyses of the sex-specific trends
revealed that the declining trend in stunting was signifi-
cant only among males (F (1, 7204) = 5.79, p ≤ .016) as
was the trend for wasting (F (1, 7130) = 6.56, p ≤ .010).
The declining trends in underweight were statistically sig-
il
2
Amugsi et al. BMC Public Health 2013, 13:960 Page 4 of 16
http://www.biomedcentral.com/1471-2458/13/960Results
Participation rates
Anthropometric data (weight and length/height) were
collected from children 0–60 months in the surveys
conducted in 1998, 2003, and 2008, and from children
0–36 months in the 1993 survey. To make cross-year
comparability possible, we restricted our study sample
to children less than 3 years old. Of 2,204 children who
were part of the 1993 survey, anthropometry data were
available for 1,966 (89.2%), and of the 2,067 children
who were part of the 1998 survey, anthropometry data
were reported for 1,778 (86.0%). In the 2003 survey, of the
2,439 children in the study anthropometry data were avail-
able for 1,933 (79.3%). In 2008, 1,904 children participated
and anthropometry data were available for 1,558 (81.8%).
Trends in stunting (height-for-age)
Table 1 shows the national prevalence of stunting, under-
weight and wasting over the study period, in total and
separately for males and females, and the results of Chi
Square tests of homogeneity. The tests for homogeneity
are statistically significant (except for the test for wasting
for females), indicating that the prevalence estimates
changed significantly over the years (the Chi Square tests
Table 1 Prevalence and 95% confidence intervals (C.I.) in ch
36 Months, 1993 to 2008
1993 1998
Variables % C.I. % C.I. %
Stunting 33,5 31.4, 35.6 25,6 23.5, 27.7 33,6
Male 37.2 34.3, 40.3 28.4 25.4, 31.6 37.4
Female 29.5 26.7, 32.5 22.9 20.1, 25.8 29.8
Underweight 25,1 23.2, 27.0 19,8 17.9, 21.7 20.0
Male 27.0 24.3, 29.8 19.7 17.1, 22.5 22.6
Female 23.1 20.5, 25.8 19.9 17.3, 22.7 17.4
Wasting 14,6 13.1, 16.2 13,3 11.7, 15.0 11.0
Male 15.5 13.4, 18.0 15.0 12.7, 17.7 11.3
Female 13.6 11.6, 15.9 11.6 9.6, 14.0 10.7nificant for both males (F (1, 7441) = 26.69, p ≤ .001) and
females (F (1, 7441) = 20.14, p ≤ .001).
In the age-specific data for stunting shown in Table 2,
decline occurred primarily in the 24–35 month age
group for both males (F (1, 2171) = 15.42, p < .001) and
females (F (1, 2171) = 9.91, p ≤ .002). The slope of the
trends did not differ by sex.
In the analysis of urban/rural place of residence, shown
in Table 3, there was a stable and not statistically signifi-
cant trend for urban males and females and rural females,
while the declining trend for rural males was statistically
significant (F (1 ,5203) = 7.23, p ≤ .007). The decline for
boys contributed to an apparent narrowing of the urban/
rural gap in stunting, as shown in Figure 1.
Decomposing the analysis of stunting by regions (Table 4)
revealed that the declining trends were significant for
males only in the Northern region (F (1,931) = 3.95,
p ≤ .047) and Middle region (F (1, 1843) = 7.19, p ≤ .007),
and not significant for females in any of the regions.
The analyses of stunting by maternal education (Table 5)
reveal a narrowing in the gap in nutritional status between
various groups stratified by maternal education. This is
due in part to a significantly worsening trend in stunting
among males having mothers with higher than primary
education (F (1, 2004) = 3.92, p ≤ .048) and among females
dhood malnutrition at the national level, ages 0 through
003 2008
C.I. % C.I. p X2 Total N
31.3, 35.9 25,7 23.4, 28.2 0,001 51,472 7205
34.2, 40.7 29.2 25.8, 32.9 0.001 29.422 3603
26.8, 33.0 22.2 19.2, 25.6 0.001 23.178 3602
18.2, 21.9 15,1 13.3, 17.1 0,001 54,863 7442
20.0, 25.5 16.9 14.3, 20.0 0.001 30.072 3733
15.1, 20.0 13.2 10.9, 15.8 0.001 30.582 3709
9.6, 12.6 12,4 10.7, 14.3 0.010 11,317 7131
9.3, 13.6 13.3 10.8, 16.3 0.051 8.741 3556
8.9, 13.0 11.4 9.3, 14.0 0.281 4.121 3575
Table 2 Prevalence and 95% confidence intervals (C.I.) in stunting among Ghanaian children ages 0 to 36 months,
by age and sex, 1993 to 2008
1993 1998 2003 2008
Variables % C.I % C.I % C.I % C.I p X2 Total N
Age
0-5
Male 15.0 10.5, 21.0 7,5 4.4, 12.6 15,9 10.8, 22.8 9.1 4.7, 17.0 0,058 7.467 633
Female 12.8 8.8, 18.2 8.3 4.7, 14.1 11.0 6.8, 17.3 5.2 2.4, 11.0 0,139 5.486 626
6-11
Male 16.0 11.4, 22.1 12.2 7.7, 18.7 23.7 17.3, 31.5 16.9 11.2, 24.6 0,062 7.350 635
Amugsi et al. BMC Public Health 2013, 13:960 Page 5 of 16
http://www.biomedcentral.com/1471-2458/13/960having mothers with higher than primary education (F (1,
2004) = 4.34, p ≤ .037). This is illustrated in Figure 2.
Stratifying the analysis by urban/rural place of residence,
Table 6 shows significantly declining trends in stunting
among males of mothers with no education in the rural
sample (F (1, 2570) = 7.07, P ≤ .008), while females of
mothers with more than primary education exhibit wors-
ening trends (F (1, 1103) = 5.01, P ≤ .025). Males also ex-
hibit statistical significant increasing trends in the urban
Female 12.1 7.9, 18.1 12.8 8.4, 19.0 13.9
12-23
Male 45.4 40.1, 50.7 35.8 30.6, 41.5 43,2
Female 32.3 27.2, 37.9 25.4 20.7, 30.8 34,2
24-35
Male 55.1 49.2, 60.7 40.4 34.2, 46.8 49,9
Female 47.6 42.0, 53.3 34.5 28.8, 40.7 44,3sample (F (1, 900) = 5.80, P ≤ .016).
In the analyses of stunting by level of household wealth
(WI), shown in Table 7, statistically significant declines
were observed for females in the poorest wealth quintile
(F (1, 2111) = 4.39, P ≤ .036) and for males in the poorer
wealth quintile (F (1, 1559) = 5.99, p ≤ .014).
Trends in underweight (weight-for-age)
Table 9 shows the underweight prevalence estimates by age
and residence. The declining trend for females in the 0–5
Table 3 Prevalence in stunting among Ghanaian children by pl
1993 1998 20
% C.I % C.I %
Residence
Urban
Male 26.4 21.6, 31.9 22.5 17.2, 28.8 29.5
Female 16.6 12.5, 21.6 14.4 10.0, 20.4 22.0
Rural
Male 41.5 37.9, 45.2 30.5 27.0, 34.3 41,5
Female 34.4 30.9, 38.0 25.5 22.2, 29.0 33,6month age group was significant (F (1, 1347) = 7.47,
p ≤ .006), as was the case in the 12–23 month age group
for males (F (1, 2523) = 8.48, p ≤ .004) and for females (F (1,
2523) =5.03, p ≤ .025). The declines in underweight were
also statistically significant in the 24–25 month age group,
both for males (F (1, 2228) = 19.90, p ≤ .001) and females (F
(1, 2228) = 12.88, p ≤ .001). The stable to worsening trends
in underweight for males and females in the 6–11 month
age group did not achieve statistical significance.
9.7, 19.6 11.5 6.7, 19.1 0,923 .482 673
37.7, 48.8 37.0 30.7, 48.8 0.040 8.320 1267
28.8, 40.0 29.3 23.9, 35.5 0,096 6.351 1199
43.7, 56.0 38.4 32.0, 45.3 0,001 19.453 1068
38.4, 50.4 29.1 23.3, 35.8 0,001 23.155 1104In the analyses of underweight by rural/urban place of
residence, the declining trends were significant for rural
males (F (1, 5376) = 24.22, p ≤ .001) and rural females (F
(1, 5376) = 14.22, p ≤ .001), while the declining trends for
urban males and females were not significant. The result
was an apparent narrowing in the urban/rural under-
weight gap, as shown in Figure 3.
In the analyses by region (Table 10), significant declin-
ing trends were observed in the Middle region for males
(F (1, 1888) = 11.99, p ≤ .001) and females (F (1, 1888) =
ace of residence, 0-36 Months 1993 to 2008 with 95% CI
03 2008
C.I % C.I p X2 Total N
24.2, 35.5 25.9 24.2, 35.5 0,314 3,552 1024
16.9, 27.9 18.7 14.2, 24.4 0,142 5,452 977
37.6, 45.5 31.1 26.8, 35.7 0,001 29.329 2579
30.0, 37.4 24.4 20.6, 28.7 0,001 23,658 2625
Table 4 Trends in stunting among Ghanaian children by region, 0-36 Months 1993 to 2008 with 95% CI
1993 1998 2003 2008
% C.I % C.I % C.I % C.I p X2 Total N
Region
Upper
Male 36.8 27.8, 47.0 37.3 30.0, 45.2 40,9 31.7, 50.6 26.4 18.8, 35.8 0,331 3.423 550
Female 33.9 25.7, 43.3 28.5 22.0, 36.1 26,7 19.6, 35.2 35.1 25.8, 45.7 0,662 1,588 541
Middle
Male 40.0 34.4, 45.9 28.8 22.7, 35.7 35,1 29.3, 41.3 26.4 20.5,33.4 0,006 12.533 926
Female 30.6 25.3 , 36.5 21.3 16.1, 27.7 34.0 28.2, 40.4 22.3 16.8,29.0 0,003 14.227 918
South
Male 35.3 30.7, 40.1 27.4 22.9, 32.3 35,2 29.8, 40.9 31.5 25.7, 37.9 0,059 7.452 1312
Amugsi et al. BMC Public Health 2013, 13:960 Page 6 of 16
http://www.biomedcentral.com/1471-2458/13/9605.71, p ≤ .017) and in the Northern region for males (F
(1, 959) = 17.66, p ≤ .001). The trends in the other re-
gions did not reach statistical significance.
In the analysis of underweight by maternal education
(Table 11), there was a distinct narrowing of the gap in
Female 28.6 24.3, 33.2 23.5 19.5, 28.1 25.1
Accra
Male 22.3 15.0, 31.9 21.7 14.1, 31.8 21.9
Female 14.8 8.8, 23.8 12.2 6.7, 21.3 14,2
Northern
Male 50.0 40.9, 59.1 34.2 25.2, 44.6 58,9
Female 39.2 30.0, 49.2 35.9 26.0, 47.1 46,8underweight prevalence between children whose mothers
have no education and those whose mothers have higher
than primary education, as shown in Figure 4. This was
due in part to significant declining trends for males having
mothers with no education (F (1, 3104) = 18.95, p ≤ .001)
and for females having mothers with no education (F
(1, 3104) =10.79, p ≤ .001).
In the analysis of underweight by household wealth
(Table 12), statistically significant declining trends were
Table 5 Trends in stunting among Ghanaian children by mate
1993 1998
% C.I % C.I %
Maternal education
No education
Male 43.9 38.9, 48.9 32.3 27.5, 37.5 41,8
Female 33.2 28.7, 38.1 27.4 22.9, 32.4 37,4
Primary
Male 35.1 31.2, 39.2 33.1 26.4, 40.5 33,9
Female 28.7 25.0, 32.8 22.3 16.5, 29.4 22.6
Some high school+
Male 15.3 8.1, 26.8 22.1 17.8, 27.2 34,9
Female 9.6 4.1, 21.1 19.5 15.6, 24.1 26,2observed in the poorest wealth quintile for males (F (1,
2192) = 9.18, p ≤ .002), in the poorer wealth quintiles for
males (F (1, 1606 ) = 11.24, p ≤ .001) and females (F (1,
1606) = 5.09, p ≤ .024), in the middle quintiles for males
(F (1,1390) = 10.66, p ≤ .001) and females (F (1, 1390) =
20.6, 30.3 23.0 18.1, 29.7 0,283 3.807 1378
14.1, 32.3 24.0 13.8, 38.3 0,985 .151 324
7.9, 24.1 14.3 7.8,25.3 0,965 .272 324
50.7, 66.5 33.8 25.8, 43.0 0,001 19.648 491
38.8, 55.0 20.4 13.5, 29.5 0,001 17.451 4418.50, p ≤ .004), and in the richer quintile for females (F (1,
1230) = 4.88, p ≤ .027).
Trends in wasting (weight-for-height)
Tables 13, 14, 15, 16 and 17 show prevalence estimates for
wasting. There was a significant declining trend for males
in the 12–23 month age group (F (1, 2451) = 4.31,
p ≤ .038) and in the 24–35 age group (F (1, 2451) = 9.58,
p ≤ .002). None of the other trends had significant slopes.
rnal education, 0-36 Months 1993 to 2008 with 95% CI
2003 2008
C.I % C.I p X2 Total N
36.9, 47.0 28.6 23.1, 34.8 0,001 21.362 1502
32.6, 42.4 19.9 15.2, 25.7 0,001 22.930 1498
27.4, 41.1 34.7 27.8, 42.4 0,963 .284 1125
17.3, 29.0 24.6 18.2, 32.3 0.180 4.885 1073
29.6, 40.6 26.2 21.0, 32.1 0,001 19.950 975
21.2, 31.8 22.7 18.3, 27.8 0.020 9.867 1030
Table 6 Trends in stunting among Ghanaian children stratified by maternal education and place of residence, 0-36
Months 1993 to 2008 with 95% CI
1993 1998 2003 2008
% C.I % C.I % C.I % C.I p X2 Total N
Urban
No education
male 35.7 24.3, 49.0 33.0 21.7, 46.6 32.1 21.7, 44.6 25.4 15.5, 38.8 .69 1.60 252
female 22.0 12.6, 35.6 17.9 8.5, 33.8 35.2 22.9, 49.9 21.6 10.6, 39.1 .23 4.61 177
Primary
male 26.1 20.2, 33.2 26.9 16.4, 40.9 22.0 13.4, 33.8 31.3 20.4, 44.7 .70 1.58 361
female 18.1 13.0, 24.7 17.5 7.9, 34.3 13.0 5.9, 26.3 24.2 14.1, 38.3 .56 2.31 310
High school+
male 12.2 5.6, 24.7 12.2 7.3, 19.6 28.8 22.3, 36.4 22.2 15.7, 29.9 .004 14.29 496
female 8.1 2.6, 22.4 15.5 9.5, 24.2 21.4 14.3, 30.8 16.5 10.9, 24.2 .31 3.90 405
Rural
No education
male 43.1 37.8, 48.6 30.4 25.7, 35.5 40.8 36.1, 45.8 26.8 21.3, 33.1 .001 27.76 1467
female 39.9 31.9, 42.1 30.5 25.0, 36.5 41.5 35.5, 47.7 21.1 15.4, 28.3 .001 22.28 1104
Primary
male 38.0 33.3, 43.1 33.1 25.9, 41.1 35.9 28.5, 44.1 32.8 25.1, 41.5 .63 1.91 842
female 35.5 30.6, 40.8 24.1 16.7, 33.5 28.7 21.5, 37.3 28.0 19.5, 38.4 .11 6.51 685
High school+
male 20.0 5.0, 54.2 26.7 21.4, 32.8 37.0 30.1, 44.4 27.6 21.0, 35.4 .09 6.66 611
female 20.0 6.6, 47.1 21.5 16.3, 27.9 33.5 25.8, 42.1 31.3 23.8, 39.8 .06 7.52 493
Table 7 Trends in stunting among Ghanaian children by household wealth quintile, 0-36 Months 1993 to 2008 with
95% CI
1993 1998 2003 2008
% % C.I % C.I % C.I % C.I p X2 Total N
Wealth
Poorest
Male 51,3 44.2, 58.4 30,8 25.2, 36.9 47,2 41.2, 53.3 32,1 25.9, 39.1 0,001 27,446 1031
Female 37,1 30.7, 44.0 29,9 24.6, 35.8 39,7 34.1, 45.7 26.0 20.3, 32.6 0,009 11,667 1081
Poorer
Male 44.0 37.6, 50.6 24,9 18.9, 32.2 33,8 27.1, 41.1 34,1 26.7, 42.4 0,001 15,649 770
Female 28.3 22.5, 34.9 24.0 18.3, 30.9 29,4 23.5, 36.0 27,3 20.8, 34.9 0,671 1,549 790
Middle
Male 37,7 31.4, 44.3 35,2 28.2, 43.0 41.0 33.6, 48.8 32,1 24.4, 40.8 0,376 3,103 698
Female 36,1 29.4, 43.1 24.5 18.6, 31.7 32,1 25.2, 39.9 18.3 12.1, 26.8 0,002 14,387 654
Richer
Male 31.1 24.9, 38.0 23.0 16.8, 30.8 31,2 23.7, 39.8 20,6 14.2, 29.1 0,084 6,661 596
Female 29.2 23.3, 36.0 14,8 9.7, 21.9 23.6 16.3, 32.9 21.6 15.1, 29.8 0,016 10,285 597
Richest
Male 18.7 13.4, 25.4 25,8 18.6, 34.7 28.5 21.3, 37.0 23.4 14.9, 34.8 0,219 4,43 508
Female 13.5 9.0, 19.9 13.9 8.3, 22.3 16.9 11.1, 24.9 12.9 7.2, 22.2 0,841 0,834 480
Total sample 1934 1754 1992 1525 7205
Amugsi et al. BMC Public Health 2013, 13:960 Page 7 of 16
http://www.biomedcentral.com/1471-2458/13/960
Table 8 Trends in underweight among Ghanaian children by age, 0-36 Months 1993 to 2008 with 95% CI
1993 1998 2003 2008
Variables % C.I % C.I % C.I % C.I p X2 Total N
Age
0-5
Male 16,9 12.2, 23.0 11,8 7.6, 17.8 11.0 7.0, 16.9 9.9 6.0, 16.0 0.180 4.891 686
Female 16,9 12.3, 22.8 10.8 6.7, 16.9 7.9 4.7, 12.9 8.3 4.5, 14.6 0,026 9.269 662
6-11
Male 22.8 17.2, 29.5 19.5 13.8, 26.9 26.4 20.0, 34.1 19.2 12.8, 27.7 0,375 3.110 648
Amugsi et al. BMC Public Health 2013, 13:960 Page 8 of 16
http://www.biomedcentral.com/1471-2458/13/960Discussion
This study examined long-term trends in child mal-
nutrition among children less than three years of age
in Ghana, by region, by rural–urban setting and by
socio-economic and demographic characteristics. The
findings at the national level show that overall child
malnutrition is significantly decreasing. Stunting, the
effect of chronic under-nutrition, declined from 33.5%
in 1993 to 25.7% (23% reduction) by 2008. This is in
sharp contrast with the complete stagnation of stunting
trends in the West African Sub-region as a whole [2].
Thus, Sub-regional trends cannot be used to estimate
Female 19.9 14.5, 26.7 17.4 12.1, 24.3 19.2
12-23
Male 28.8 24.3, 33.9 23.9 19.4, 29.0 27.0
Female 24.8 20.3, 30.1 24.0 19.5, 29.2 19,6
24-35
Male 33.9 28.7, 39.5 19.4 14.9, 24.8 22,3
Female 27.2 22.5, 32.6 21.9 17.3, 27.5 18,6trends in countries within a region, at least not in the
case of Ghana. Yet, given the current trend, the rate of
decline in stunting is not likely to move Ghana to the
level where it will meet the WHO target of a 40% re-
duction by 2025 [35].
Child underweight has also declined significantly in
Ghana, from 25.1% in 1993 to 15.1% in 2008, a 60% re-
duction. Thus, by 2008 Ghana had already achieved the
Millennium Development Goal (MDGs) target of halving
Table 9 Trends in underweight among Ghanaian children by p
1993 1998 20
% C.I % C.I %
Residence
Urban
Male 18.0 13.9, 22.9 12.3 8.4, 17.5 17,3
Female 14.3 10.6, 19.1 15.7 11.2, 21.6 9,9
Rural
Male 30.6 27.3, 34.0 22.4 19.3, 25.8 25,3
Female 26.4 23.3, 29.8 21.2 18.2, 24.5 21,11990 levels of underweight by 2015. This achievement
could be attributed to significant reduction in extreme
poverty in Ghana over the past two decades. For in-
stance, overall poverty declined from 51.7% in 1991 to
28.5% in 2006 [36,37]. Similarly, the number of people
living below the extreme poverty line in the country de-
creased by more than half in the last two decades. Total
food production also increased significantly in Ghana
during this period [36], essential for an increase in the
availability of food to the population. Another possible
explanation of the achievement made by Ghana with re-
gard to underweight reduction is the introduction of the
14.2, 25.5 14.4 9.3, 21.7 0,635 1.709 693
22.3, 32.2 17.8 13.2, 23.7 0,013 10.793 1298
15.5, 24.5 16.7 12.7, 21.8 0,055 7.613 1226
17.7, 27.7 18.9 14.2, 24.7 0,001 22.797 1101
14.4, 23.5 10.8 7.3, 15.7 0,001 21.961 1128National Plan of Action on Nutrition (NPAN) by the Gov-
ernment of Ghana in 1995 [38], which aims to combat
malnutrition in the country. The NPAN is multimodal, in-
cluding the Baby–Friendly Health Facility Initiative, the
Community Based Nutrition Behavior Change Communi-
cation (BCC) strategy, mother-to-mother support groups
for promoting optimal breastfeeding and complementary
feeding practices, and community-based growth promo-
tion projects, are a few of the NPAN’s key elements.
lace of residence, 0-36 Months 1993 to 2008 with 95% CI
03 2008
C.I % C.I p X2 Total N
13.2, 22.3 14.6 10.5, 20.0 0,244 4.168 1051
6.8, 14.2 10.4 7.2, 14.7 0.120 5.826 1014
22.0, 28.9 18.3 14.9, 22.2 0,001 26.488 2682
18.1, 24.4 14.9 11.9, 18.5 0,001 22,331 2695
Table 10 Trends in underweight among Ghanaian children by region, 0-36 Months 1993 to 2008 with 95% CI
1993 1998 2003 2008
% C.I % C.I % C.I % C.I p X2 Total N
Region
Upper
Male 36.5 27.4, 46.5 29.0 22.5, 36.5 40.6 32.2, 49.5 23.6 15.9, 33.5 0,093 6,41 587
Female 33.3 25.2, 42.6 24.1 18.1, 31.2 21,6 15.4, 29.6 23.6 15.9, 33.6 0,257 4.044 567
Middle
Male 24.6 19.9, 29.9 19.7 14.7, 26.0 19.0 14.6, 24.2 12.5 8.1, 18.9 0,011 11,2 947
Female 22.8 18.1, 28.3 20.6 15.5, 26.8 17,5 13.2, 22.8 12.6 8.6, 18.1 0,028 9,074 942
South
Male 23,1 19.3, 27.4 17,7 14.0, 22.1 18,8 14.7, 23.8 18,2 13.8, 23.6 0,195 4,697 1358
Amugsi et al. BMC Public Health 2013, 13:960 Page 9 of 16
http://www.biomedcentral.com/1471-2458/13/960The situation for wasting is not as positive as it is for
stunting and underweight. Even though Ghana has made
some progress in the reduction of wasting between 1993
and 2008, the national prevalence remains high at 12.4%
for children less than 36 months, a level the WHO clas-
sifies as requiring urgent response [39]. Wasting, which
Female 19.7 16.1, 23.9 19.2 15.6, 23.5 14,3
Accra
Male 17.0 10.7, 26.0 10.6 5.6, 19.2 13,9
Female 12.5 7.0, 21.2 11.9 6.5, 20.7 5,1
Northern
Male 46.4 37.8, 55.6 36.5 27.2, 46.9 34,1
Female 35.7 26.9, 45.7 31.5 22.2, 42.5 32,6is usually the result of acute significant food shortage
and/or disease, is a strong predictor of mortality among
children under-five years [18]. Hence, for Ghana to be
on track to achieve MDG 4, there is a need for re-
doubled effort to address childhood wasting.
A number of important departures from the national
trends raise cause for concern. The situation at the re-
gional level is a complete departure from the national
level. Our results show that the Middle region is the only
Table 11 Trends in underweight among Ghanaian children by m
1993 1998
% C.I % C.I %
Maternal education
No education
Male 36.3 31.6, 41.3 25.5 21.3, 30.4 27,8
Female 28.8 24.5, 33.4 24.1 19.9, 29.0 23,6
Primary
Male 22.6 19.3, 26.2 20.0 14.7, 26.6 21,4
Female 20.3 17.1, 24.0 17.3 12.2, 23.9 14,6
Some high school+
Male 9.7 4.4, 19.9 14.0 10.6, 18.4 17,8
Female 7.4 2.8, 18.2 17.6 13.9, 22.0 12,4region that exhibited significantly declining trends in
stunting and underweight both for both males and fe-
males, revealing that not all regions enjoyed the declining
trends seen in the national-level analyses. In terms of
prevalence, the Northern and Upper regions have the
highest levels of malnutrition, and this has also been doc-
10.9, 18.6 11.5 8.2, 15.9 0,006 12.438 1411
7.8, 23.7 7.8 3.2, 17.8 0,278 3,852 333
1.9, 13.0 6.5 2.7, 15.0 0,123 5.772 337
26.9, 42.1 23.9 17.0, 32.4 0,003 13,99 508
25.6, 40.6 20.0 13.3, 29.0 0,067 7.151 452umented in previous studies [5,14]. The disproportionate
burden of malnutrition on children in the Northern part
of Ghana may be attributable the North’s high rate of pov-
erty [36,37]. While the reduction in overall poverty at the
national level between 1992 and 2006 was remarkable, the
three Northern regions did not record significant poverty
reduction [36,37]. Additionally, over 70% of people whose
incomes are below the poverty line can be found in the
Savannah areas [36,37]. The observed improvement in
aternal education, 0-36 Months 1993 to 2008 with 95% CI
2003 2008
C.I % C.I p X2 Total N
23.6, 32.4 20.8 16.1, 26.4 0,001 20.687 1560
19.7, 28.0 15.3 11.2, 20.4 0,002 14,97 1545
16.2, 27.8 16.2 11.4, 22.4 0,256 4.048 1159
10.4, 20.1 12.4 8.3, 18.1 0,057 7,531 1097
13.8, 22.7 14.5 10.6, 19.6 0.280 3.830 1013
9.1, 16.7 12.2 9.0, 16.3 0,047 7.941 1066
Table 12 Trends in underweight among Ghanaian children by household wealth quintile, 0-36 Months 1993 to 2008
with 95% CI
1993 1998 2003 2008
% C.I % C.I % C.I % C.I p X2 Total N
Wealth
Poorest
Male 34,4 28.0, 41.4 29,3 23.9, 35.3 30,5 25.4, 36.0 20,2 15.2, 26.3 0,017 10,231 1080
Female 28,6 22.8, 35.2 25,2 20.3, 30.9 28,8 23.8, 34.4 19.0 14.2, 24.8 0,077 6,838 1113
Poorer
Male 32,6 26.8, 39.0 16,8 11.8, 23.2 23,9 18.2, 30.8 18,9 13.0, 26.7 0,001 16,434 797
Female 29,3 23.5, 35.9 23,6 17.9, 30.5 17,5 13.0, 23.1 16,6 11.7, 23.1 0,007 12,084 810
Middle
Male 33,9 28.0, 40.5 22,5 16.7, 29.6 22,5 16.8, 29.5 21,2 15.0, 29.2 0,009 11,491 718
Female 25,6 20.0, 32.2 15,9 11.2, 22.0 15,9 10.9, 22.5 7,7 4.0, 14.6 0,001 17,99 673
Richer
Amugsi et al. BMC Public Health 2013, 13:960 Page 10 of 16
http://www.biomedcentral.com/1471-2458/13/960poverty statistics at the national level has not been equi-
Male 17,9 13.2, 24.0 14.0 9.2, 20.7 19,5
Female 18,8 13.9, 24.9 19,6 13.8, 27.2 10,8
Richest
Male 12,8 8.6, 18.7 10,2 5.8, 17.3 11,2
Female 10,1 6.2, 15.8 9,8 5.4, 17.2 6,8
Total sample 1970 1795 2062tably distributed. As poverty is a strong driver of mal-
nutrition [40], sustained high poverty rates in the North
translate into sustained malnutrition in the children of the
North. Poverty and malnutrition are mediated by in-
adequate food consumption and infectious disease. In-
sufficient availability of food at household level due to
poverty and/or improper feeding practices places children
at an elevated risk of malnutrition, both directly and by
Table 13 Trends in wasting among Ghanaian children by age
1993 1998 20
Variables % C.I % C.I %
Age
0-5
Male 17,5 12.6, 23.8 18.1 12.5, 25.4 13.5
Female 17,3 12.5, 23.3 11.0 6.8, 17.5 18.0
6-11
Male 22.0 16.5, 28.8 23.7 17.3, 31.7 20.0
Female 18.3 13.1, 25.0 21.3 15.4, 28.7 19.2
12-23
Male 16.7 13.1, 21.1 14.4 10.8, 18.9 11.0
Female 13.7 10.2, 18.2 12.2 8.8, 16.5 9,1
24-35
Male 8.8 6.0, 12.8 9.0 5.9, 13.5 6,2
Female 8.5 5.8, 12.2 5.5 3.2, 9.2 3,5malnutrition’s influence on a child’s susceptibility to dis-
13.5, 27.2 10,5 6.1, 17.5 0,132 5,619 616
6.5, 17.5 9,5 5.7, 15.5 0,017 10,186 615
6.8, 18.0 10,3 5.6, 18.4 0,871 0,711 522
3.5, 12.5 8,8 4.4, 17.0 0,685 1,486 498
1615 7442ease and infection, and immune system dysfunction [41].
It is clear from the above discussion that the overall
positive national trends mask important variation at the
sub-national level. This demonstrates that sub-national
level analyses are important for identifying regions and
social groups that need better support and interven-
tions to improve the malnutrition situation. Further,
there is evidence that even the regional level prevalence
, 0-36 Months 1993 to 2008 with 95% CI
03 2008
C.I % C.I p X2 Total N
8.7, 20.2 19.3 12.8, 28.1 0,534 2.188 616
12.5, 25.3 17.8 11.6, 26.2 0,333 3.407 614
14.1, 27.7 24.1 16.8, 33.3 0.850 .799 620
14.1, 25.5 19.7 13.6, 27.6 0,921 .492 674
8.0, 15.0 12.9 8.8, 18.6 0,171 5.009 1261
6.2, 13.3 10.9 7.6, 15.3 0,351 3.278 1191
3.9, 9.6 4.7 2.7, 8.2 0,179 4.903 1059
1.9, 6.4 3.5 1.7, 7.1 0,038 8.448 1096
data may mask differences in other important segments
of the country [42].
Our data suggest that differentials in child malnutri-
tion by place of residence have substantially narrowed
over time in Ghana, consistent with other studies [15].
This trend in urban–rural differentials is primarily due
to static trends in urban malnutrition coupled with rural
Therefore, most antipoverty initiatives are directed at the
rural population. However, the emphasis on the alleviation
of rural poverty has led to a degree of neglect of the prob-
lems of urban poverty and urban food insecurity. In fact,
urban poverty and associated health problems are growing
in Ghana [44,45]. The introduction of nutrition rehabilita-
tion services and the Supplementary Feeding and Health
Table 14 Trends in wasting among Ghanaian children by place of residence, 0-36 Months 1993 to 2008 with 95% CI
1993 1998 2003 2008
% C.I % C.I % C.I % C.I p X2 Total N
Residence
Urban
Male 11.6 8.3, 15.9 9.0 5.7, 13.9 8,5 5.6, 12.7 11.1 7.3, 16.6 0,549 2.115 1001
Female 8.9 6.0, 13.0 8.6 5.3, 13.7 9,4 6.4, 13.5 9.6 6.5, 13.9 0,981 .178 977
Rural
Male 17.1 14.5, 20.1 17.2 14.4, 20.5 12,7 10.3, 15.6 14.6 11.5, 18.4 0,078 6,821 2555
Female 15.4 12.9, 18.3 12.6 10.2, 15.4 11,4 9.1, 14.2 12.6 9.8, 16.0 0,166 5,078 2598
gi
20
Amugsi et al. BMC Public Health 2013, 13:960 Page 11 of 16
http://www.biomedcentral.com/1471-2458/13/960improvement. A number of factors might have contrib-
uted to the improving trends in rural areas. One such
factor is the significant increase in food production in
Ghana between 1992 and 2008 [36,37]. Since much of the
food consumed in Ghana is produced in the rural areas, it
is plausible that food supplies became increasingly avail-
able to rural households during this period, and when
food is available in the household, it is assumed that
children will be well fed. Another possible explanation for
the urban–rural differentials is that poverty is viewed
in Ghana as an overwhelming rural phenomenon [43].
Table 15 Trends in wasting among Ghanaian children by re
1993 1998
% C.I % C.I %
Region
Upper
Male 26.0 18.2, 35.7 15.6 10.6, 22.3 21,3Female 16.8 10.9, 25.2 10.9 6.9, 16.8 14,2
Middle
Male 14.0 10.4, 18.6 14.7 10.2, 20.5 9,9
Female 14.7 10.9, 19.6 9.9 6.4, 15.1 9,6
South
Male 12.8 9.8, 16.5 16.3 12.8, 20.8 11,1
Female 11.2 8.4, 14.7 13.1 10.0, 16.9 11,4
Accra
Male 12.9 7.5, 21.4 6.1 2.6, 13.9 9,4
Female 5.7 2.4, 13.0 7.3 3.3, 15.4 6,9
Northern
Male 22.1 15.4, 30.7 21.2 13.9, 30.8 9,8
Female 24.2 16.6, 33.8 15.4 8.9, 25.2 12.4and Nutrition Education Programmes [38], could also ex-
plain the narrowing gap between rural and urban settings.
These programmes are targeted at deprived communities,
mostly rural communities. These explanations are sens-
ible, but remain merely speculative, because the possible
contributory factors identified above were not measured
in the surveys that provided the data for this study.
The gap in malnutrition by maternal education level
has significantly narrowed in Ghana, to the extent that
malnutrition rates for children of mothers who have no
education are indistinguishable from children of educated
on, 0-36 Months 1993 to 2008 with 95% CI
03 2008
C.I % C.I p X2 Total N
14.4, 30.3 15.4 9.0, 25.0 0,243 4.179 5469.1, 21.4 18.0 11.8, 26.4 0,728 1.303 537
6.8, 14.2 11.9 7.5, 18.4 0,332 3,417 918
6.5, 14.1 12.8 8.6, 18.7 0,247 4,141 913
8.0, 15.2 11.8 8.1, 16.9 0,165 5.099 1293
8.3, 15.4 9.5 6.4, 13.8 0,508 2.326 1358
4.5, 18.6 9.5 3.9, 214 0,481 2.471 312
3.3, 14.0 2.8 .70, 10.8 0,429 2.766 326
6.0, 15.7 20.3 13.8, 28.8 0,056 7.566 487
8.0, 18.8 18.2 11.9, 26.7 0,125 5.732 441
ones in 2008. These differences in trends are partly due to
increasing malnutrition in children of mothers with more
than primary education. Further analysis stratified by place
of residence was undertaken as part of this study, to exam-
ine the possibility that the effect of education is con-
maternal education to have the expected effect on child
nutritional status in Ghana could be due to structural fac-
tors. One such factor is intervention targeting. Most nutri-
tion and antipoverty interventions in Ghana are targeted at
the population considered disadvantaged, in this case, the
Table 16 Trends in wasting among Ghanaian children by maternal education, 0-36 Months 1993 to 2008 with 95% CI
1993 1998 2003 2008
% C.I % C.I % C.I % C.I p X2 Total N
Maternal education
No education
Male 20.1 16.3, 24.5 20.2 16.2, 25.0 12,7 9.7, 16.5 18.8 14.0, 24.6 0,031 8,905 1477
Female 17.8 14.3, 22.0 12.4 9.2, 16.5 12,8 9.8, 16.4 15.3 11.2, 20.6 0,153 5.267 1486
Primary
Male 13.4 10.8, 16.6 13.4 9.0, 19.6 11,6 7.7, 17.1 10.2 6.3, 16.0 0,671 1,548 1106
Female 11.0 8.6, 14.1 12.4 8.1, 18.6 11,3 7.6, 16.6 8.4 5.2, 13.4 0,681 1,505 1066
Some high school+
Male 6.8 2.6, 16.7 11.1 8.0, 15.2 9,7 6.8, 13.5 11.2 7.6, 16.1 0,704 1.407 972
Female 7.7 2.9, 18.8 10.6 7.8, 14.4 8,2 5.5, 11.9 10.2 7.3, 14.4 0,643 1.671 1022
se
Amugsi et al. BMC Public Health 2013, 13:960 Page 12 of 16
http://www.biomedcentral.com/1471-2458/13/960founded by place of residence. This analysis did not
provide evidence of such confounding.
The findings above are unexpected because it is as-
sumed that mothers who have high education will be
more empowered to be able to take decisions on the type
of nutrition and care the child should receive. Educated
mothers could also be in a better position to make in-
formed nutritional decisions that buck unhealthy cultural
norms about child feeding [46]. The puzzling failure of
Table 17 Trends in wasting among Ghanaian children by hou
1993 1998
% C.I % C.I %
Wealth
Poorest
Male 14,8 10.3, 20.7 24.0 18.9, 30.1 11,6
Female 17,6 12.9, 23.5 14,5 10.6, 19.5 15,8
Poorer
Male 16,4 12.0, 21.9 13,4 8.9, 19.8 16.0
Female 18.0 13.3, 24.0 15,3 10.6, 21.6 7,8
Middle
Male 21,5 16.5, 27.5 15,6 10.7, 22.1 9,6
Female 14.6 10.3, 20.3 8.0 4.8, 13.0 8.0
Richer
Male 14,4 10.0, 20.1 11,4 7.0, 18.0 11,6
Female 10.3 6.7, 15.4 9,4 5.5, 15.7 9.1
Richest
Male 9.0 5.5, 14.4 5,7 2.6, 11.8 6,5
Female 5,8 3.0, 10.8 8,7 4.5, 16.1 11,6
Total sample 1913 1742 1957non-educated and rural population [38]. This places the
so-called advantaged groups, who also have pressing needs,
at a disadvantage. The re-coding of the education variable
could also be a plausible explanation. Our data have few
people who have higher education and as a result, second-
ary education was collapsed with higher education. This
could mask the importance of higher maternal education
as a protective factor. Additionally, it is important to note
that Ghana has very high unemployment rates [36,37], and
hold wealth quintile, 0-36 Months 1993 to 2008 with 95% CI
2003 2008
C.I % C.I p X2 Total N8.3, 16.0 13,9 9.6, 19.8 0,003 14,036 1016
11.9, 20.7 15,9 11.6, 21.5 0,844 0,823 1075
11.4, 22.1 15,1 9.6, 22.9 0,848 0,806 759
4.8, 12.5 12,5 8.1, 18.8 0,021 9,712 777
6.0, 14.9 19,6 13.2, 27.9 0.010 11,348 692
4.8, 13.1 11,5 6.5, 19.6 0.100 6,251 646
7.0, 18.5 9,2 5.2, 15.9 0,523 2,245 592
5.2, 15.6 8.1 4.4, 14.3 0,891 0,624 597
3.2, 12.7 6.0 2.5, 13.8 0,638 1,693 497
7.0, 18.5 6,4 2.9, 13.7 0,285 3,788 480
1519 7131
010
20
30
40
50
60
1993 1998 2003 2008
Pr
ev
al
en
ce
(%
)
year
Total stunting Urban, males Urban, females
Rural, males Rural, females
Amugsi et al. BMC Public Health 2013, 13:960 Page 13 of 16
http://www.biomedcentral.com/1471-2458/13/960as a result most mothers with secondary education are un-
likely to find a decent job to earn a living, and conse-
quently may find it difficult to provide for the basic needs
of their children.
Our data show that child malnutrition trends in the
poorest wealth quintiles are decreasing while trends in the
richest quintiles remain static. Even though this suggests
that children in the poorest quintiles are getting better
over time, malnutrition remains the bane of the poor in
Ghana. The results show that malnutrition prevalence
among children in the poorest wealth quintile is as much
as twice that of the richest quintile. These findings cor-
roborate earlier studies in Ghana using DHS data, which
found that children in the poorest households are more
than twice at risk of being malnourished compared to
their counterparts in the richest households [5,23]. How-
Figure 1 Stunting trends by rural/urban.ever, the declining trends among the poorest quintiles im-
plies that the halving of people living below the extreme
0
10
20
30
40
50
60
1993 1998
Pr
ev
al
en
ce
(%
)
Y
Total stunting
No education, females
Higher than primary, female
Figure 2 Stunting trends by maternal education.poverty line, and the significant increase in food produc-
tion in Ghana between 1991 and 2008 [36,37] have helped
to close the malnutrition equity gap.
The child’s age is an important factor in level of risk for
malnutrition. Malnutrition is more prevalent in older than
in younger children [5-7]. In our data, the oldest children
(24–35 months) have the highest levels of malnutrition,
and the youngest age group (0–5 months) has the lowest
levels. With regard to time trends, the older children
exhibited significant improvement compared to the youn-
ger ones in the case of stunting. However, both younger
and older age groups exhibited significant declining trends
in the case of underweight. The higher proportion of mal-
nutrition among older children could be due to inappro-
priate child feeding practices and/or increased morbidity,
while the declining malnutrition trends may be explained
by systematic improvement in the availability and quality
of food [36,37] and other care practices during this period.
2003 2008
ear
No education, males
Higher than primary, males
s
05
10
15
20
25
30
35
40
1993 1998 2003 2008
Pr
ev
al
en
ce
(%
)
Year
Total underweight Urban, males Urban, females
Rural, males Rural, females
Amugsi et al. BMC Public Health 2013, 13:960 Page 14 of 16
http://www.biomedcentral.com/1471-2458/13/960This is an empirical issue that could be addressed using
the GDHS data on child care and feeding; however such
analyses are beyond the scope of this paper.
There are limitations in the analysis reported in this
paper. The core data of this study come from the an-
thropometry measurements and the birth date data used
to calculate the growth variables. As reported in our re-
sults, data of sufficient quality was obtained from be-
tween 87 and 90 percent of eligible children depending
on survey year. There are a myriad reasons why useable
anthropometry data might be missing, discussed in detail
by Pullum [47]. These include poor technical work by
data collectors, faulty equipment, sick or uncooperative
children, refusal by the mother or another household
member, the child being away from the household dur-
ing the data collection window, and data entry errors at
Figure 3 Underweight trends by rural/urban.the time of data collection and/or in the transfer of data
to analyzable data files, among other reasons. While
0
5
10
15
20
25
30
35
40
1993 1998
Pr
ev
al
en
ce
(%
)
Y
Total underweight
No education, females
Higher than primary,females
Figure 4 Underweight trends by maternal education.missing data is of inevitable concern in survey research,
what would be of greater concern would be a systematic
pattern over the survey years wherein various reasons
for missing data increased or decreased in prevalence
from survey to survey. We do not have detailed enough
missing data analyses from the four surveys to evaluate
the seriousness of this potential source of bias. There is
evidence that poor birth data was a minor cause of miss-
ing growth data in all the survey.
Besides missing data, the validity of the malnutrition
trends reported in this paper may be compromised due
to method variation in determining which children were
eligible for measurement. In 1993 and 1998 surveys, an-
thropometric measurements were restricted to children
born to the women who were interviewed. Children
were excluded if their mothers were not in the house-
hold, if their mothers were not eligible for the individual
interview, or the mother did not complete an interview.
2003 2008
ear
No education, males
Higher than primary,males
stunting among children of mothers who have higher than
Amugsi et al. BMC Public Health 2013, 13:960 Page 15 of 16
http://www.biomedcentral.com/1471-2458/13/960primary education, in sharp contrast with existing litera-
ture. This anomaly needs further investigation.
Competing interest
The authors declare that they have no competing interest.
Authors’ contributions
DAA and MBM designed the study. DAA performed the data analysis,
interpreted the results and drafted the manuscript. MBM supervised all parts of
the study and contributed to the methodology and revision of the manuscript.
AL contributed to the planning of the study and revision of the manuscript. All
authors read and approved the final version of the manuscript.
Acknowledgements
The authors thank MEASURE DHS for releasing the data for this study. We
also wish to thank the Ghana Statistical Service and Ghana Health Service
who were responsible for collecting the data. Our thanks also go to Stein
Atle Lie of Uni health for assistance with data analysis, to Dorcus Mbithe forThe methodology changed in the 2003 and 2008 surveys,
and children who slept in the household the night before
data collection were eligible regardless of the interview
status of their mother. As a result, orphans and children
whose mothers were away were excluded in 1993 and
1998 and included in 2003 and 2008. This would pose a
validity issue if orphans, for example, are more likely to
suffer malnourishment than non-orphans in the same
household. At least one study has examined this issue,
comparing South African orphans and non-orphans in
the same households [48]. There were no significant dif-
ferences between the two groups on health outcomes.
Nevertheless, the change in the sampling protocol in
2003 is a source of concern for trend analyses such as
this paper reports.
Conclusions
The analysis at the national level shows that child malnu-
trition is significantly declining in Ghana. However, the ag-
gregate national trends mask the fact that not all segments
of the population benefit from improvement of the same
magnitude, as our findings by various geographic and
demographic characteristics reveal. There is a need for pol-
icies that address the specific constraints of households left
out of progress so that children from all segments of the
country benefit. Additionally, the study also found that the
widened gap between rural and urban settings in Ghana is
closing. While child malnutrition is progressively decreas-
ing in the rural areas, it remains static in the urban set-
tings. This contributed greatly to narrowing the rural/
urban child malnutrition gap. In addition, the malnutrition
trends by maternal education in Ghana have narrowed to
the extent that the differences between the educated and
non-educated are not easily distinguishable. A perplexing
finding of this study is the increasing trends of childhoodher contribution during the planning stages of the study, and to Department
of Health Promotion and Development colleagues Helga Urke and Dennis
Matanda for fruitful discussions which helped to shape the study.Author details
1Department of Health Promotion and Development, Faculty of Psychology,
University of Bergen, Christiesgt. 13, 5020 Bergen, Norway. 2Department of
Nutrition and Food Science, University of Ghana, Legon, Accra, Ghana.
Received: 8 March 2013 Accepted: 8 October 2013
Published: 16 October 2013
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doi:10.1186/1471-2458-13-960
Cite this article as: Amugsi et al.: An analysis of socio-demographic
patterns in child malnutrition trends using Ghana demographic and
health survey data in the period 1993–2008. BMC Public Health
2013 13:960.Submit your manuscript at 
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