How well do WHO complementary feeding indicators relate to nutritional status of children aged 6–23 months in rural Northern Ghana?
Saaka et al. BMC Public Health
How well do WHO complementary feeding indicators relate to nutritional status of children aged 6-23 months in rural Northern Ghana?
Mahama Saaka 0
Anthony Wemakor 0
Abdul-Razak Abizari 0
Paul Aryee 0
0 University for Development Studies, School of Medicine and Health Sciences , P O Box TL 1883, Tamale , Ghana
Background: Though the World Health Organization (WHO) recommended Infant and Young Child Feeding (IYCF) indicators have been in use, little is known about their association with child nutritional status. The objective of this study was to explore the relationship between IYCF indicators (timing of complementary feeding, minimum dietary diversity, minimum meal frequency and minimum acceptable diet) and child growth indicators. Methods: A community-based cross-sectional survey was carried out in November 2013. The study population comprised mothers/primary caregivers and their children selected using a two-stage cluster sampling procedure. Results: Of the 1984 children aged 6-23 months; 58.2 % met the minimum meal frequency, 34.8 % received minimum dietary diversity (≥4 food groups), 27.8 % had received minimum acceptable diet and only 15.7 % received appropriate complementary feeding. With respect to nutritional status, 20.5 %, 11.5 % and 21.1 % of the study population were stunted, wasted and underweight respectively. Multiple logistic regression analysis revealed that compared to children who were introduced to complementary feeding either late or early, children who started complementary feeding at six months of age were 25 % protected from chronic malnutrition (AOR = 0.75, CI = 0.50 - 0.95, P = 0.02). It was found that children whose mothers attended antenatal care (ANC) at least 4 times were 34 % protected [AOR 0.66; 95 % CI (0.50 - 0.88)] against stunted growth compared to children born to mothers who attended ANC less than 4 times. Children from households with high household wealth index were 51 % protected [AOR 0.49; 95 % CI (0.26 - 0.94)] against chronic malnutrition compared to children from households with low household wealth index. After adjusting for potential confounders, there was a significant positive association between appropriate complementary feeding index and mean WLZ (β = 0.10, p = 0.005) but was not associated with mean LAZ. Conclusions: The WHO IYCF indicators better explain weight-for-length Z-scores than length-for-age Z-scores of young children in rural Northern Ghana. Furthermore, a composite indicator comprising timely introduction of solid, semi-solid or soft foods at 6 months, minimum meal frequency, and minimum dietary diversity better explains weight-for-length Z-scores than each of the single indicators.
Child Nutritional status; Appropriate complementary feeding; IYCF indicators; Composite indicator; Northern Ghana
Chronic childhood malnutrition remains one of the most
intractable public health problems throughout the
developing world including Ghana [
]. There are several
determinants of under-nutrition, including poor dietary
practices. Appropriate complementary feeding and
caring practices by caregivers however, remain a challenge
for most households, especially in low-income countries
]. Inappropriate feeding practices in combination with
other causes such as infection and food shortage may be
responsible for one-third of malnutrition, depending on
population, place, time and season [
about 6 % of under-five deaths can be prevented
particularly in the developing world if optimal complementary
feeding is ensured, thereby contributing towards the
realization of the Millennium Development Goal 4 [
The World Health Organization (WHO) developed eight
core Infant and Young Child Feeding (IYCF) indicators to
monitor and guide the feeding practices of young children
] namely: (1) early initiation of breastfeeding; (2)
exclusive breastfeeding under six months; (3) continued
breastfeeding for one year; (4) the introduction of solid,
semi-solid or soft foods; (5) minimum dietary diversity; (6)
minimum meal frequency; (7) minimum acceptable diet;
and (8) consumption of iron-rich or iron fortified foods.
The IYCF indicators [minimum dietary diversity (MDD),
minimum meal frequency (MMF), minimum acceptable
diet (MAD)] are more related to adequate complementary
A pooled Demographic and Health Survey (DHS)
analyses from 14 countries showed that children aged 6–23
months who did not meet MAD were found to be
significantly associated with stunting whereas there was no
relationship between subnormal MMF and stunting
whilst higher dietary diversity was reported to be
strongly associated with higher length-for-age z-scores
Higher dietary diversity has been shown to be
associated with increased nutrient adequacy of diets of
children and adults in developed countries [
associated with increased micronutrient density of foods
consumed or better child nutritional status in developing
The association between recommended IYCF indicators
and child nutritional status has not yet been explored in
Northern Ghana, where the prevalence of stunting is
highest in Ghana and overall dietary quality is likely to be poor.
This analysis was undertaken to determine the association
between World Health Organization (WHO)
recommended IYCF indicators and nutritional status of children
6–23 months of age in selected communities in Northern
Ghana comprising Northern Region, Upper East Region
and the Upper West Region. The specific IYCF indicators
investigated are minimum dietary diversity, minimum
meal frequency and minimum acceptable diet. We also
assessed the relationship between child growth indicators
in this sample with a composite indicator designed to
measure appropriate complementary feeding.
The nutrition surveillance covered 44 districts in the
three regions of Northern Ghana comprising the
Northern Region (NR), Upper East Region (UER) and Upper
West Region (UWR). The three regions share some
boundaries with each other. The under-five population
for the three regions is estimated to be 1,308,742 [
Majority of the people (60.0 %) have agriculture as
their main occupation while some are involved in
trading. The main staple foods including maize, sorghum,
millet and yam are usually harvested from October
through December. Although the food security situation
is usually good during the harvesting time, child care
tends to suffer because of lack of time on the part of
Survey design, population and sampling
In this study, regionally representative nutrition
surveillance data were collected in November, 2013 in a
crosssectional nutrition survey. In this stratified cluster survey,
each of the three regions was considered a stratum, and a
number of clusters per stratum selected randomly using
the probability proportionate to size (PPS) technique.
Women of reproductive age and their children 6–23
months old in the sampled households were included in
The main outcome variable used to calculate the
sample size was prevalence of chronic malnutrition which
was 37.4 % in Northern Region, 31.5 % in Upper East,
and 23.1 % in Upper West (UNICEF MICS, 2011).
Aiming at an absolute precision of 5 % at the 95 %
confidence level, further assuming a correction factor of 2.0
(the “design effect”) for cluster sampling, and allowing
for 5 % refusals and incomplete questionnaires, the
required minimum sample sizes (n) were 756, 697 and 573
for Northern, Upper East and Upper West regions
The sample size was estimated using OpenEpi
software for epidemiologic statistics version 3.01.
There is a minimum number of clusters that should be
included in each stratum for the survey to be
considered valid [
]. Usually, 25 clusters are considered a
In each cluster, a complete list of all households was
compiled and systematic random sampling used in
selecting study households. All the households in each
cluster were serially numbered. To get the sampling
interval, the total number of households in a cluster was
divided by the sample size of 20. The first household
was then randomly selected by picking any number
within the sample interval. Subsequent selections were
made by adding the sampling interval to the selected
number in order to locate the next household to visit. If
the selected household did not have a target respondent,
then next household was selected using the systematic
sampling procedure. This process continued until the
required sample size was obtained. A minimum of 20
mother/child pairs were randomly selected from a
cluster. Only one eligible participant was selected from each
household for interview, using simple random sampling.
Quantitative data were collected using structured
questionnaire in face-to-face interviews during
house-tohouse visits. Socioeconomic and demographic
characteristics of participants, child’s age, gender, morbidity in
the past week, child feeding practices, and child
anthropometry data were also collected. Details of data
collected are contained in the Additional file 1.
The data were collected by interviewers who had
completed at least Senior Secondary School and who
underwent intensive training for two days on the content of
the questionnaire and on general approaches to data
Independent and dependent variables
WHO IYCF indicators [minimum dietary diversity
(MDD), minimum meal frequency (MMF), minimum
acceptable diet (MAD)] and a child feeding index (CFI)
were the main independent variables. The main
dependent variable was child nutritional status which
was treated as both continuous and categorical variables.
The continuous variables were length -for-age Z-score
(LAZ), weight-for- length Z-score (WLZ) and
weightfor-age Z-score (WAZ). Categorical variables were
stunting, wasting and underweight which reflect LAZ, WAZ
and WLZ below −2 standard deviations below the
Other confounders included (i) age and gender of the
child; (ii) maternal education, and utilization of prenatal
care; (iii) and household wealth status. A brief
description of main independent and dependent variables is as
Assessment of IYCF Practices
Infant and young child feeding indicators including
minimum meal frequency, minimum dietary diversity
and minimum acceptable diet were estimated by recall
of food and liquid consumption during the previous day
of the survey as per WHO/UNICEF guidelines [
Minimum meal frequency is the proportion of children
who received complementary foods at least the
minimum recommended number of times in the last
24 hours. A child was judged to have taken ‘adequate
number of meals if he/she received at least the
minimum frequency for appropriate complementary feeding
(that is, 2 times for 6–8 months and 3 times for 9–11
months, 3 times for children aged 12–23 months) in last
24 hours. For non-breastfed children, the minimum
meal frequency was 4.
The WHO defined minimum dietary diversity as the
proportion of children aged 6–23 months who received
foods from at least four out of seven food groups [
The 7 foods groups used for calculation of WHO
minimum dietary diversity indicator are:
(i) grains, roots and tubers; (ii) legumes and nuts; (iii)
dairy products; (iv) flesh foods; (v) eggs; (vi) vitamin A
rich fruits and vegetables; and (vii) other fruits and
The dietary diversity score therefore ranged from 0–7
with minimum of 0 if none of the food groups is
consumed to 7 if all the food groups are consumed.
Additionally, the individual dietary diversity score
(IDDS) of the children was also determined based on 14
food groups as recommended by the Food and
Agriculture Organization (FAO) [
]. The food groups are
cereals, vitamin A rich vegetables and tubers, white roots
and tubers, dark green leafy vegetables, other vegetables,
Vitamin A rich fruits, other fruits, organ meat (iron
rich), flesh meat, eggs, fish, legumes, nuts and seeds,
milk and milk products, and oils and fats. Based on
these food groups, the dietary diversity score therefore
ranged from 0–14 with minimum of 0 if none of the
food groups is consumed to 14 if all the food groups are
consumed. For comparison reasons, these food items
were re-grouped into seven food groups according to
WHO infant feeding guidelines.
From the dietary diversity score, the minimum dietary
diversity indicator was constructed using the WHO
recommended cut-off point with a value of “1” if the child
had consumed four or more groups of foods and “0” if
less. Minimum dietary diversity is the proportion of
children who ate at least 4 or more varieties of foods from
the seven food groups in a 24-hour time period [
Minimum acceptable diet is a composite indicator of
minimum dietary diversity and minimum meal
frequency. Breastfed children who meet both the minimum
diversity and the minimum meal frequency are
considered to have met the WHO recommended minimum
Previous studies have described complementary
feeding practice using single indicators but a combination of
indicators is needed to better explain the role of
complementary feeding practices in child growth. To adequately
quantify appropriate complementary feeding, we used a
composite indicator comprising three of the WHO core
IYCF indicators that relate closely to complementary
feeding. These are timely introduction of solid,
semisolid or soft foods at 6 months, meeting minimum meal
frequency, and minimum dietary diversity. Appropriate
complementary feeding was thus defined in this study as
when the child met all the following three criteria:
i. Complementary feeding commenced at 6th month
ii. Minimum dietary diversity
iii. Minimum meal frequency
Nutritional Status Assessment
Anthropometric indicators of length -for-age (LAZ),
weight-for-age (WAZ), and weight-for-length (WLZ)
were determined as recommended by the World Health
The questionnaire and anthropometric assessment was
carried out by well-trained health and nutrition
personnel. The age of the child was determined based
on the date of birth (obtained from child health records
booklets, birth certificates and baptismal cards) and the
date of the survey. Provision was made to use calendar
of events to estimate age of the child in the absence of
Length, weight, and upper mid-arm circumference
were obtained using standardized techniques and
equipment. Recumbent length was measured to the nearest
0.1 cm with subjects in a lying position. The crown-heel
length was taken using an infantometer (a flat wooden
surface with head and foot boards). The child was placed
on its back between the slanting sides. The head was
placed so that it is against the top end. The knees were
gently pushed down by a helper. The foot-piece was
then moved toward the child until it presses softly
against the soles of the child's feet and the feet are at
right angles to the legs. The weight in light clothes was
obtained using a digital weighing scale (SECA 890) to
the nearest 0.1 kg. The mid-upper arm circumference
(MUAC) was measured in centimeters, to the nearest
0.1 cm, using standard MUAC measuring tape for
Assessment Socio-economic Status
Principal Component Analysis (PCA) was used to
determine household socioeconomic status (wealth index)
from modern household assets namely, the presence of
electricity, type of cooking fuel, material of the dwelling
floor, source of drinking water, type of toilet facility, and
possession of household items including computer,
radio, television, refrigerator, bicycle, motorcycle/scooter,
car/truck and mobile phone [
Data Quality Control Measures
In an effort to collect quality data, a number of strategies
were applied. A two-day training session aimed at ensuring
the reliability and validity of data collected was organized for
data collectors. The training ensured a good understanding
and acquisition of skills for effective and efficient
administration of the data collection tools. The content of the training
included the aim of study, survey methodology including
selection of eligible participants, data recording,
administration of questionnaires and supervision. In addition, the
training focused on the art of interviewing and clarifying
questions that were unclear to the respondents.
The final stage in the training of data collectors involved
field-testing of data collection tools. The main aim here
was to refine the tools and to ensure the competence of the
data collectors. The household questionnaires were
pretested and revised before the main field work commenced.
In each team, there was a supervisor who ensured that
all the methodological issues were being adhered to.
Furthermore, field supervisors checked all data collected in
a given day and made sure that all field challenges were
attended to immediately in the field. Any errors noted
were discussed with the concerned enumerators. Briefing
meetings took place every day where teams shared their
experiences in the field and necessary corrections and
recommendations made to ensure smooth
implementation of the survey. In addition, the Survey Coordinator
visited teams in the communities at random to observe
how interviews were conducted.
Statistical Data Analyses
The analysis of data took into account the complex
design of multi-stage cluster surveys. All quantitative data
were coded for statistical analysis using SPSS Complex
Samples module for windows 18.0 (SPSS Inc, Chicago).
This was done in order to make statistically valid
population inferences and computed standard errors from
sample data. Design weights were added to each region
(that is, total population divided by number of
respondents) to perform weighted analysis.
The Emergency Nutrition Assessment (ENA) for
SMART software (2010 version) was used for the
anthropometric data analysis and reported using WHO
2006 growth reference values with SMART cut-offs.
Both bivariate and multivariate analyses were
performed to identify the determinants of stunting. Firstly,
bivariate analyses for all the various risk factors were
performed using Chi-square (χ2) tests and ANOVA. The
association between dependent variables (stunting and
wasting) and independent variables was determined
using multiple logistic regression modeling, which
included all potential socioeconomic, and demographic
confounders that were significant at P values < 0.05 in
the bivariate analysis. The logistic regression outputs
were presented as adjusted odds ratios (AOR) with 95 %
confidence intervals (CI).
Before testing for associations between independent
variables and the dependent outcomes WLZ, LAZ and
WAZ, the data were tested for dependencies, intra-class
correlations and clustering effects between the different
]. Additionally, the data were cleaned and
outliers removed. Z-scores which were outside the
WHO flags: WLZ −5 to 5; LAZ −6 to 6; and WAZ −6 to
5 were excluded from the data set.
Underweight was defined as (WAZ < −2 SD), acute
malnutrition (WLZ < −2 SD) and chronic malnutrition
(LAZ < −2 SD).
The permission to carry out this study was sought from
district health authorities and the study protocol was
approved by the School of Medicine and Health Sciences of
the University for Development Studies, Ghana. Informed
consent was also obtained verbally after needed
information and explanation. Participation was voluntary and each
woman signed (or provided a thumb print if she was
uneducated) a statement of an informed consent after which
she was interviewed.
The assessment was made on a total of 2,026 mother/child
pairs. There were missing values on a number of variables
including age of children not within the age range (6–23
months), length, weight and implausible anthropometric
indices. During the data cleaning process 42 (2.1 %) cases
were excluded from this analysis and this yielded a refined
sample size of 1984. The results of this study were
presented in line with STROBE Statement—Checklist of items
that should be included in reports of cross-sectional
studies. Details of the checklist is shown in an Additional file 2.
The mean ± SD age of the children was 14.4 ±
5.2 months with 67.9 % being in the age group 12–23
months. The mean age of the mothers was 28.3 ±
6.7 years and 68.9 % had no formal schooling. The
sample comprised of boys (52.3 %) and girls (47.7 %).
Table 1 presents the summary statistics on key
characteristics of mother/child pairs in the sample.
Nutritional Status of Children 6–23 Months
Among the study population 20.5 %, 11.5 % and 21.1 %
were stunted, wasted and underweight respectively
(Table 2). The proportion of children suffering from
acute, chronic malnutrition and underweight vary
according to age group. The prevalence of stunting was
significantly higher among older children whereas global
acute malnutrition (GAM) levels were highest and
critical in the younger age group 6–11 months.
Assessment of complementary feeding practices
The proportion of children 6–23 months who met the
minimum dietary diversity (≥4 food groups) was 34.8 %
and 58.2 % had adequate meal frequency. Only 27.8 % of
the children aged 6–23 months met the minimum
acceptable diet. Children who met the acceptable diet and
having started complementary feeding at six months
were considered to have appropriate complementary
feeding. The overall appropriate complementary
prevalence was therefore 15.7 %. Younger children were more
likely to be bottle-fed in the past 24 hours than older
Compared to children aged 9–23 months, younger
children aged 6–8 months were less likely to meet
recommended minimum meal frequency, minimum diet
diversity and minimum acceptable diet (Table 3).
Association between WHO IYFC indicators and child growth
Apart from timely initiation of complementary feeding
at six months, none of the WHO recommended IYCF
indicators including bottle feeding, minimum dietary
diversity, minimum meal frequency and minimum
acceptable diet were associated with LAZ scores (Table 4).
The rest of the observed feeding patterns were not
significantly associated with stunting.
The correlation between chronic malnutrition and
IYCF indicators remained weak and non-significant for
different age groups (6–8, 9–11 and 12–23 months)
Acute malnutrition was weakly positively associated
with minimum dietary diversity and minimum
acceptable diet but associated strongly with a composite
indicator measuring appropriate complementary feeding
(Table 6). Though timing of first complementary food at
6 months was positively correlated with LAZ, it was not
associated with WLZ at all.
Determinants of Under-nutrition among Children aged
Table 7 shows bivariate analyses of predictors of chronic
malnutrition among children aged 6–23 months.
Chronic malnutrition (stunting) was less prevalent in
Christian homes. The prevalence of stunting was
significantly higher among male and older children.
Surprisingly, none of the World Health
Organization (WHO) recommended complementary feeding
indicators (Minimum meal frequency, minimum
dietary diversity, and minimum acceptable diet) was
associated with stunted growth among children aged 6–23
Multiple logistic regression analysis revealed that
children's age, ANC attendance, gender of child, and timely
introduction of first complementary food were
significantly related to stunting (Table 8). Compared to
children who were introduced to complementary either late
or early, children who started complementary at six
months of age were 25 % protected from chronic
malnutrition (AOR = 0.75, CI = 0.50 - 0.95, P = 0.02).
Compared to children aged 6–8 months, children aged 12–
23 months were 2.9 times more likely [AOR 2.98; 95 %
CI (1.91 - 4.64)] of becoming stunted. It was found that
children whose mothers attended antenatal care (ANC)
at least 4 times were 34 % protected [AOR 0.66; 95 % CI
(0.50 - 0.88)] against stunted growth compared to
children born to mothers who attended ANC less than 4
times. Male children were 1.5 times more likely [AOR
1.50; 95 % CI (1.18 - 1.90)] of being stunted compared
to female children. Children from high household wealth
index were 51 % protected [AOR 0.49; 95 % CI (0.26
0.94)] against chronic malnutrition compared to children
from low household wealth index.
Religion of mother, educational attainment of mother,
salt iodine content and CWC attendance were however
not strong predictors in the multiple regression analyses.
It is notable that the amount of residual (portion of
variability in chronic malnutrition not explained by the
model is quite large) meaning that chronic malnutrition
is explained by a number of other variables not in the
equation. The set of variables in the equation accounted
for only 6 % of the variance (Nagelkerke R Square =
0.06) in chronic malnutrition.
Multiple regression analysis revealed that the
significant independent determinants of mean WLZ were
Appropriate complementary feeding (%)
Timing of first complementary food at 6 months
Minimum meal frequency
Minimum dietary diversity
Less than 4 groups
At least 4 groups
Minimum acceptable diet
Appropriate complementary feeding
gender of the child, diarrhoeal infection, and educational
level of the mother and appropriate complementary
feeding score (Table 9). Female children were generally
heavier by 0.05 standard units (beta = 0.05, p = 0.03).
Diarrhoeal infection was the prominent determinant
of WLZ and children without diarrhoea had 0.1
standard units higher than children with diarrhoea in
the past two weeks prior to the study (beta
coefficient = 0.1, p < 0.001). Children of mothers of high
educational level (at least Senior High Secondary
School) had higher mean WLZ compared to children
of mothers who had no formal education. The
multivariable analysis also showed that, after adjusting for
potential confounders, there was a significant positive
association between the appropriate complementary
feeding index and mean WLZ (β = 0.04, p = 0.05).
This study sought to explore the relationship between
IYCF indicators (timing of complementary feeding,
minimum dietary diversity, minimum meal frequency and
minimum acceptable diet) and child growth indicators.
The main finding was that with the exception of timely
initiation of complementary feeding at 6 months, none
of the WHO core IYCF indicators associated with mean
length-–for-age z-score. Secondly, a composite indicator
comprising timely introduction of solid, semi-solid or
soft foods at 6 months, minimum meal frequency, and
minimum dietary diversity better explains
weight-forlength Z-scores of young children in rural Northern
Ghana than each of the individual indicators.
In this study a child was considered to have received
appropriate complementary feeding if he/she was
breastfeeding at the time of study and met the
minimum dietary diversity in the past 24 hours; had
adequate meal frequency in the past 24 hours prior to the
study and complementary foods were introduced at six
months. Sub-optimal feeding practice was defined as
lack of compliance to any of these recommended
In our study the WHO IYCF indicators could better
explain weight-for-height Z-scores than length-for-age
Z-scores. This is understandable because the IYCF
indicators are calculated based on 24 hours reference period
which best reflect recent diet. Given that stunting
reflects long-term cumulative nutritional status of
individuals, it is not surprising that the indicators were more
associated with wasting. Our findings also underscore
greater utility of composite indicators compared to the
The results of present study revealed that poor timing
of complementary food was associated with stunting.
Similar results have been reported from Zambia where
there was a strong positive association between
introduction of solid and semi-solid foods between 6–8
months of age and LAZ but not in Ethiopia [
χ2 = 9.4 p = 0.038
χ2 = 31.0 p <0.001
χ2 = 11.5 , p = 0.001
χ2 = 5.5 , p = 0.01
χ2 = 8.7 , p = 0.03
χ2 = 6.7 , p = 0.03
χ2 = 5.6 , p = 0.04
χ2 = 12.3 , p = 0.003
χ2 = 5.4 , p = 0.02
significant positive association between timely start of
complementary feeding and higher height-for-age
zscores has been demonstrated in other studies [
The association between nutritional status and timing of
complementary feeding could be explained by the fact
that early initiation of complementary feeding has a
potential negative effect on breastfeeding frequency and
duration. Also as the child is introduced to
complementary feeding late, children may suffer from inadequate
energy intake since breast milk alone would not be
enough after six months.
Higher dietary diversity score (DDS) was associated
with higher LAZ in Ethiopia and Zambia [
WAZ associated with DDS in both countries. Low diet
diversity is also reported to be associated with stunting
in other studies [
]. In the study that was carried
out in Zambia and Ethiopia using DHS data, adequate
dietary diversity scores, consumption of a minimally
acceptable diet, and consumption of iron -rich foods
were positively associated with LAZ.
A study conducted in Burkina Faso stated that
dietary diversity scores and frequency of meals were
positively associated with stunting [
]. Arimond and
Ruel, using evidence from a meta-analysis of 11
demographic and health surveys (DHS), have also
reported positive association between child dietary
diversity and nutritional status that is independent of
socioeconomic factors [
]. Another study in rural
Bangladesh demonstrated that reduced dietary
diversity was a strong predictor of stunting among
children < 60 months of age [
It is important to note however that these studies did
not use the new WHO recommended dietary diversity
indicator. In most of the studies dietary diversity score
was measured differently and treated as a quantitative
variable and not as minimum dietary diversity (a
categorical variable). For example, in the Bangladesh study,
dietary diversity score (DDS) was constructed through
the summation of the number of days each of the nine
food groups was consumed in the previous week. This
perhaps explains why our findings are on the contrary.
The fact that in most situations, higher LAZ had been
associated with dietary diversity scores (DDS) but not
with minimum dietary diversity, suggests that there may
be something wrong with cut-off of 4 used to categorize
minimum dietary diversity.
Gender of child
The association between child growth indicators and
child feeding indicators is not unequivocal because of
the mixed results reported from many countries. There
is no doubt that dietary diversity indicators are
associated with child nutrition outcomes, like stunting,
underweight, and wasting, but it is not the case everywhere.
Our data did not show any association between
minimum dietary diversity or minimum meal frequency and
stunting. Obviously, diet is only one aspect of what
makes children grow and dietary diversity may not be
the most pressing constraint in some areas, especially
where rates of infections are very high. In these contexts,
poor health may be a more important determinant of
nutritional status than food insecurity.
Interestingly, most of the WHO recommended
complementary feeding indicators (Minimum meal
frequency, minimum dietary diversity, and minimum
acceptable diet) were not associated with child growth
indicators among children aged 6–23 months. The
apparent lack of association may be due to the fact there
was very little variation in the study population with
respect to these indicators. The lack of association may
also be explained partly by the fact that the feeding
indicators may not be sensitive to chronic under-nutrition
because they are assessed based on 24- hour recall which
may not give the usual dietary intake.
Findings about the relationship between feeding
practices and growth have been mixed. Consistent
with the findings of this study, none of the WHO
IYCF indicators was associated with LAZ in recent
studies in Cambodia, South Ethiopia [
survey conducted in Mexico found that measures of
recommended breastfeeding and complementary
feeding practices were not associated with growth when
family economics and other factors were included in
logistic regression models .
Factors other than IYCF were identified to be
associated with both chronic and acute undernutrition. Our
findings indicated that the risk of stunting increases with
age, consistent with other studies [
]. Children in
the youngest age group 6–11 months had a significantly
lower risk of stunting than children in the older age
groups. It is likely that breastfeeding during early life is
protective and that stunting becomes more likely as the
child becomes more dependent on foods that are of poor
quality and exposure to non-food factors including
Male children were more likely to be stunted than
their counterpart females, a finding that has been
reported by earlier studies in other African countries
]. The exact factors contributing to this male
vulnerability is unclear but it is unlikely to be the
result of gender preference [
]. The male
vulnerability to malnutrition may be biological and the fact
that male infants are at greater risk of infection
because of greater tendency to explore the environment
compared to female counterparts. It has been
suggested that despite the improvement in medical care,
environmental stresses have harsher effects on males
than females in early life .
Mothers who had completed secondary school were
more likely to have children with higher WLZ, similar to
other studies [
]. Other factors that were associated
with stunting in bivariate analysis but failed to reach
significance level in multivariable regression analyses
included frequency of attending growth monitoring
sessions, mothers’ educational level, and the iodine
content of household salt.
The provision of adequate, safe and acceptable
complementary food is essential in order to reduce child
undernutrition. It is for this reason, WHO and UNICEF
have recommended eight core infant feeding practices to
be adopted [
]. To better promote these recommended
practices, it is essential to demonstrate the evidence on
the existing proportion of mothers who are adopting
these dietary practices and the effect they have on
growth. Findings from this survey showed the
proportion of children aged 6–23 months receiving the
recommended diets was below expectations.
The cross-sectional nature of the data limits our ability
to draw any causal conclusions since the problem of
endogeneity cannot be ruled out. Recall bias was also
possible and may affect the validity of the findings.
Despite these limitations, our results have shed more light
on the association between the current WHO
recommended indicators and the nutritional status of children
aged 6 to 23 months in developing country setting.
Conclusions and Recommendations
With the exception of timely initiation of
complementary feeding at 6 months, none of the WHO core IYCF
indicators associated with mean height-for-age z-score.
However, minimum meal frequency, minimum dietary
diversity and minimum acceptable diet were either not
or only weakly correlated with WLZ scores for all
children aged 6–23 months. These indicators associated
more with acute malnutrition than chronic malnutrition.
A composite indicator comprising timely introduction of
solid, semi-solid or soft foods at 6 months, minimum
meal frequency, and minimum dietary diversity better
explains weight-for-length Z-scores of young children in
rural Northern Ghana than each of the individual
A prospective cohort study should be conducted to
better elucidate the relationship between the child
feeding indicators and chronic malnutrition.
Additional file 1: Nutrition surveillance system in northern ghana
Ghs/unicef. (DOCX 53 kb)
Additional file 2: STROBE Statement—Checklist of items that
should be included in reports of cross-sectional studies. (DOC 86 kb)
ANOVA: Analysis of variance; ANC: Antenatal care; AOR: Adjusted odds ratio;
CFI: Child feeding index; CI: Confidence interval; DDS: Dietary diversity score;
DHS: Demographic and health surveys; ENA: The Emergency Nutrition
Assessment; GAM: Global acute malnutrition; IYCF: Infant and young child
feeding; LAZ: Length -for-age Z-score; MAD: Minimum acceptable diet;
MDD: Minimum dietary diversity; MMF: Minimum meal frequency;
MUAC: Mid-upper arm circumference; PCA: Principal Component Analysis;
PPS: Probability proportionate to size; SMART: Standardized Monitoring and
Assessment of Relief and Transitions; WAZ: Weight-for-age Z-score;
WLZ: Weight-for-length Z-score; WHO: World Health Organization.
The authors declare that they have no competing interests.
MS, AW, AA and PA conceived the study, participated in its design and
contributed significantly to the acquisition of data. MS and AW did the
analysis and interpretation of data and were deeply involved in drafting the
manuscript and revising it critically for important intellectual content. All the
authors read and approved the final draft.
The authors wish to express their sincere gratitude to the data collection
team members for their hard work and commitment, and for working
tirelessly during the data collection phase. The data could not have been
obtained without the co-operation and support of the programme communities,
especially the caregivers who took time off from their busy schedules to respond
to the interviewers. Their involvement and cooperation is highly appreciated.
This acknowledgement will be incomplete if the dedicated work of the data
entry and analysis team is not mentioned.
Finally, we wish to acknowledge the financial support from United Nations
Children’s Fund UNICEF, without which the survey would not have been
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