Matanda et al. BMC Pediatrics 2014, 14:5
http://www.biomedcentral.com/1471-2431/14/5
RESEARCH ARTICLE
Open Access
Child undernutrition in Kenya: trend analyses
from 1993 to 2008–09
Dennis J Matanda1*, Maurice B Mittelmark1 and Dorcus Mbithe D Kigaru2
Abstract
Background: Research on trends in child undernutrition in Kenya has been hindered by the challenges of changing
criteria for classifying undernutrition, and an emphasis in the literature on international comparisons of countries’
situations. There has been little attention to within-country trend analyses. This paper presents child undernutrition
trend analyses from 1993 to 2008–09, using the 2006 WHO criteria for undernutrition. The analyses are decomposed by
child’s sex and age, and by maternal education level, household Wealth Index, and province, to reveal any departures
from the overall national trends.
Methods: The study uses the Kenya Demographic and Health Survey data collected from women aged 15–49 years
and children aged 0–35 months in 1993, 1998, 2003 and 2008–09. Logistic regression was used to test trends.
Results: The prevalence of wasting for boys and girls combined remained stable at the national level but declined
significantly among girls aged 0–35 months (p < 0.05). While stunting prevalence remained stagnant generally, the
trend for boys aged 0–35 months significantly decreased and that for girls aged 12–23 months significantly increased
(p < 0.05). The pattern for underweight in most socio-demographic groups showed a decline.
Conclusion: The national trends in childhood undernutrition in Kenya showed significant declines in underweight
while trends in wasting and stunting were stagnant. Analyses disaggregated by demographic and socio-economic
segments revealed some significant departures from these overall trends, some improving and some worsening.
These findings support the importance of conducting trend analyses at detailed levels within countries, to inform
the development of better-targeted childcare and feeding interventions.
Keywords: Undernutrition, Wasting, Stunting, Underweight, Trends, Demographic and Health Survey, Kenya
Background
Worldwide, about 2.2 million children die annually, with
poor nutritional status as an underlying cause [1]. Global
statistics for surviving undernourished children indicate
that approximately 171 million children are chronically
undernourished (stunted), 60 million are acutely undernourished (wasted), and 100 million are underweight [2].
Undernutrition is not only linked to child mortality but
also to poor functional development of the child. Undernourished children are highly susceptible to common
childhood ailments like diarrhea, respiratory infections
and worm infestations. Recurrence of such ailments falters a child’s physical, behavioral, motor and cognitive
development, and also compromises her/his health and
* Correspondence: matandajd@gmail.com
1
Department of Health Promotion and Development, University of Bergen,
P.O.Box 7807, NO-5020, Christiesgt. 13 Bergen, Norway
Full list of author information is available at the end of the article
functioning in adulthood [3]. Combatting child undernutrition is obviously crucial, and its complexity makes it
hard to tackle. It results not only from macronutrient
deficiencies (protein, fat and carbohydrate) but also from
micronutrient deficiencies (trace minerals and vitamins),
among which zinc deficiency is particularly deleterious
to children’s normal growth [4]. Therefore, different
aspects of food deprivation (quantity, quality and food
group diversity) lead to different manifestations of undernutrition (wasting, stunting and underweight). Consequently, child undernutrition is a multidimensional
problem that defies simple solutions. There is a fundamental need to better understand the public health dimensions
of the problem, to provide a foundation for precisely targeted interventions in local contexts.
The burden of child undernutrition is unsurprisingly
greatest in the world’s poorest countries, especially in
© 2014 Matanda 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.
Matanda et al. BMC Pediatrics 2014, 14:5
http://www.biomedcentral.com/1471-2431/14/5
sub-Saharan Africa and Asia [5]. This is a highly salient
issue in Kenya, which is among the 20 countries that account for 80% of the world’s chronically undernourished
children [6]. The most recent Kenyan national prevalence estimates are 35% for stunting, 7% for wasting and
16% for underweight [7,8].
A child who experiences a chronic shortage of appropriate types and quantities of food is likely to grow in
height/length more slowly than expected for children of
the same sex and age. Such a shortfall in growth, termed
‘stunting’, is a classical indicator of underlying child undernutrition. A child who experiences acute food shortage and/or infection is likely to gain weight more slowly
than expected for children of the same sex and height/
length. Such a shortfall in growth is termed ‘wasting’,
which is also a classical indicator of underlying undernutrition. Underweight is a composite indicator of stunting
and wasting and thus an overall indicator of the extent
of child undernutrition [9,10]. Underweight however is
not a very useful indicator for interventions as it does
not differentiate the extent of stunting and wasting.
According to the World Health Organisation (WHO)
2006 classification of child undernutrition, children with
a Z-score below −2 Standard Deviations (SD) of the
median for weight-for-height/length (WHZ) or (WLZ),
height/length-for-age (HAZ) or (LAZ) and weight-forage (WAZ) are classified as wasted, stunted and underweight respectively. Children with a Z-score below −3 SD
of the median are classified as severely undernourished,
while those with a Z-score between −2 SD and −3 SD are
classified as being moderately undernourished. Those with
a Z-score between −1 SD and −2 SD are classified as mildly
undernourished [9].
There is a tendency in the literature to define and describe child undernutrition at an aggregate level, for example by reporting national prevalence for all children
ages 0–59 months, without differentiation by age, sex
and other factors. Yet, important differentiations do exist
for specific demographic and socio-economic segments
in the under-five population and nutrition interventions
are correspondingly specific. In sub-Saharan Africa, boys
have consistently posted higher rates of stunting compared to girls [11]. Many (sometimes contradicting) reasons have been hypothesized to explain the sex difference,
such as gender-differentiated feeding practices [12,13]. It
is also postulated that girls are physically less active and
therefore spend less energy compared to boys, and that
boys are more vulnerable to acute respiratory infection
and diarrhoea [14].
Undernutrition is most critical during the first two
years of life, especially stunting, after which it is difficult
to restore normal growth [15]. During this early period,
poor infant and young nutrition and care practices
coupled with infectious diseases increase the probability of
Page 2 of 13
child undernutrition [16]. Studies conducted in developing
countries indicate that exclusive breastfeeding is not common, with complementary foods introduced very early
[17,18]. This leads to faltering child growth [19].
Level of maternal education has been documented as a
determining factor in child undernutrition. In an environment with sufficient resources, mothers with education
are more likely to utilize modern health care and have
good health care knowledge and reproductive behaviours
[20,21]. Maternal education does not, however, automatically impart nutrition knowledge, and thus mothers with
education may still have undernourished children.
Given this background, it cannot be assumed that
international or national trends reflect sub-group trends
with validity; it is an empirical question requiring appropriate sub-group analyses. This study therefore aimed to
describe time trends in child undernutrition prevalence
in Kenya, with overall trends decomposed by age, provincea, urban/rural residence, maternal education level
and Wealth Index (WI), for boys and girls separately.
Previous studies which have examined sub-groups in
Kenya are inadequate for today’s needs for one or more of
these reasons: the design was a single cross-sectional survey and therefore not useful to define trends over time; the
study sample was not nationally representative data; the
study was conducted before 2006 and hence used outdated reference standards for child growth [17,22-26].
The present study addresses these limitations, by
undertaking trend analyses of stunting, wasting and
underweight, in defined sub-groups in Kenya, and using
the 2006 WHO child growth standards in the analysis of
data collected from four cross-sectional surveys conducted
in 1993, 1998, 2003 and 2008–09. The surveys used identical methods, making their results comparable.
Methods
Data
This study used data from the Kenya Demographic and
Health Survey (KDHS), a series of national cross-sectional
surveys conducted in 1993, 1998, 2003 and 2008–09 (data
from KDHS earlier than 1993 are not used). These datasets
are publicly accessible through application to MEASURE
DHSb [26]. In all survey years, data were collected using
identical questionnaire items for women of reproductive
age 15–49 years old. In all four surveys, a standard child
anthropometry protocol was used. Children 0–59 months
were weighed using scales fitted with a digital screen and
measured for height using a measuring board. Weight was
recorded in kilograms and height/length in centimeters.
Children younger than 24 months were measured lying
down on the board (recumbent length), while standing
height was recorded for older children. Extensive information on data collection and management has been published elsewhere [7,27-30].
Matanda et al. BMC Pediatrics 2014, 14:5
http://www.biomedcentral.com/1471-2431/14/5
Page 3 of 13
Table 1 shows the two-stage sampling design used by
the Kenya Demographic and Health Survey. The first
stage involved selecting data collection points (clusters)
from the national master sample frame and then households were systematically sampled from the selected
clusters with women of ages 15–49 years eligible for
interview [7,27-29].
To enable a trend analysis, variables of interest were
identified in the base year data file (1993). Thereafter,
data files were sorted by their identification variables
and the four cross-sectional datasets of 1993, 1998, 2003
and 2008–09 were merged into a single data file. Besides
examining trends for the samples as wholes, sub-group
analyses were undertaken, separately for boys and girls,
by age, province, residence, maternal education and WI.
In each trend analysis, logistic regression was used to
test the null hypothesis that the regression coefficient β
for survey year was not significantly different from zero,
using the equation:
log ðp=1−pÞ ¼ β0 þ βsurvey year survey year
Due to lack of anthropometry data for children older
than 36 months in the 1998 survey, the analysis reported in this paper was restricted to children aged
0–35 months. This allowed comparability of trends across
the four surveys from 1993 to 2009. The age categories
analyzed were 0–5 months, 6–11 months, 12–23 months
and 24–35 months. During the 1993 and 1998 survey
years, KDHS did not collect data in North-Eastern province. Consequently, North-Eastern province was excluded
in the analysis in order to allow comparison of prevalence
across all the four survey years. Provinces included in the
analysis include Nairobi, Central, Coast, Eastern, Nyanza,
Rift-Valley and Western.
Self-reported maternal education level was categorized
as no education, incomplete primary, complete primary
and incomplete secondary education. Sample size limitations in the 1993 survey for the higher education category
were overcome by combining the complete secondary education and higher education categories in the analyses presented in this paper.
Standard of living measurement involved classification
of children into quintiles based on the household Wealth
Index. This is a proxy for standard of living based on
household ownership of assets and housing quality. Each
asset is assigned a factor score generated through principal component analysis, with the scores summed and
standardized. All individuals are assigned the score and
the quintile (poorest, poorer, middle, richer and richest)
of their household [31].
Child anthropometry
In assessing children’s nutritional status, wasting (low
weight-for-length/height), stunting (low length/heightfor-age) and underweight (low weight-for-age) were used
as the three indicators of child undernutrition. In conformity with the recommended World Health Organization
(WHO) child growth standards of 2006, the SPSS syntax
file ‘igrowup_DHSind.sps’ was used to calculate Z-scores
for the three anthropometric indicators. Children were
considered wasted, stunted or underweight if their WHZ/
WLZ, HAZ/LAZ and WAZ score was less than −2 SD respectively. Extreme Z-scores considered to be biologically
implausible were flagged and not used in the analysis if
WHZ/WLZ score was less than −5 SD or greater than
5 SD, HAZ/LAZ score was less than −6 SD or greater
than 6 SD and WAZ score was less than −6 SD or greater
than 5 SD [32,33].
Analysis
SPSS for windows version 19 was used to conduct the
analyses. The design effect parameters ‘sampling weight’,
‘sample domain’ and ‘sample cluster’ [32] were incorporated using SPSS’ Complex Samples Module. In line with
recommendations that emphasize provision of levels of
uncertainty in the estimates of undernutrition [33], 95%
confidence intervals (C.I.) for the prevalence estimates
were computed and are presented in Tables 2, 3, 4. Logistic regression was used to test trends. This involved
modeling change in undernutrition prevalence regressed
on time (the four survey years) with probability values
for Wald F tests less than 0.05 considered significant
(Tables 2, 3, 4). It is important to note that in the Tables,
the 95% C.I. are calculated separately for each prevalence estimate and are not associated with the Wald F
statistics that were generated by the logistic regression
tests for trends.
Results
Description of the study samples
Table 1 Sampling design, KDHS
1993
1998
2003
2008-09
Clusters Selected
536
536
400
400
Households Selected
8805
9465
9865
9936
Women Interviewed
7540
7881
8195
8444
Response Rate
95%
96%
94%
96%
Table 5 shows the sample distributions for each year by
child’s growth, sex and age, and by province, urban/rural
residence, maternal education and Wealth Index. Sample
sizes in the various socio-demographic groups varied considerably, affecting the comparability of the Wald F Statistics generated by logistic regression in the tests of trends
(shown in Tables 2, 3, 4). This variability should be kept in
mind in the examination of the data in Tables 2, 3, 4.
Matanda et al. BMC Pediatrics 2014, 14:5
http://www.biomedcentral.com/1471-2431/14/5
Page 4 of 13
Table 2 Wasting trends by age, province, residence, maternal education and wealth index, KDHS
1993
Sex n
1998
%
2003
C.I.
n
2,921 8.7
7.6-10.0
7.5-10.9
1,501 9.2
5.8-9.3
1,420 8.2
9.7
6.3-14.7
273
160
8.3
4.8-14.1
367
10.4 7.3-14.6
%
2008-09
C.I.
n
M/F 2,969 8.4
7.2-9.8
M
1,789 9.1
F
1,180 7.3
0-5 months
M
275
F
6-11 months
M
F
179
7.0
4.0-12.0
232
9.9
6.4-15.2
221
4.7
2.6-8.3
185
6.5
3.2-13.0
0.056
0.812
12-23 months
M
648
10.2 7.4-14.0
528
9.1
6.8-12.1
638
8.9
6.6-12.0
583
5.3
3.6-7.8
5.714
0.017
F
360
8.9
5.9-13.3
489
9.8
7.1-13.3
385
6.4
4.3-9.5
430
4.2
2.7-6.6
8.977
0.003
24-35 months
M
500
6.2
4.3-9.0
407
4.6
2.8-7.4
472
6.1
4.0-9.4
543
9.2
5.8-14.3
3.139
0.077
F
482
6.0
4.0-8.8
520
5.9
4.0-8.6
426
3.5
2.0-6.1
464
4.4
2.4-8.0
1.431
0.232
M
58
4.8
1.3-16.3
F
43
0.0
M
199
3.5
F
159
4.6
M
146
13.7 9.5-19.4
126
7.4
4.4-12.2
167
8.2
5.0-13.2
173
13.7 8.9-20.5
0.012
0.913
F
93
13.9 8.3-22.3
112
6.4
3.7-10.8
97
4.7
2.1-10.0
108
11.8 7.2-18.7
0.172
0.679
M
344
10.5 7.0-15.6
230
6.8
4.0-11.3
321
7.9
4.7-12.8
268
4.8
2.7-8.2
3.981
0.048
F
248
11.7 7.3-18.0
259
6.4
3.3-12.1
184
2.7
1.0-7.5
233
5.7
2.8-11.1
3.583
0.060
M
292
9.6
6.7-13.6
312
12.9 9.0-18.2
278
6.3
3.7-10.6
385
6.1
4.1-9.0
6.403
0.012
F
189
5.2
2.6-10.0
297
11.2 8.1-15.4
201
1.5
0.5-5.0
222
4.5
2.3-8.7
3.496
0.063
M
418
10.9 7.2-16.3
396
7.8
5.4-11.2
520
12.6 9.0-17.5
533
13.4 10.0-17.8 1.794
0.182
F
268
7.5
4.8-11.6
340
7.7
5.3-11.2
323
6.3
3.9-10.2
318
6.4
3.6-11.2
0.375
0.541
M
333
6.8
4.4-10.4
193
6.9
4.0-11.8
239
8.3
4.5-14.9
242
3.8
1.6-8.8
0.777
0.379
F
181
4.2
2.0-8.6
203
7.2
4.0-12.6
163
7.8
4.7-12.6
158
1.5
0.5-4.8
1.199
0.275
Urban
M
192
6.4
3.8-10.7
283
7.9
4.8-12.7
324
5.6
3.6-8.6
305
7.4
4.7-11.5
0.001
0.973
F
135
5.9
3.0-11.5
244
7.9
4.8-12.9
222
3.8
1.7-8.6
244
3.5
1.8-6.6
3.611
0.058
Rural
M
1,598 9.4
7.7-11.4
1,218 9.5
7.8-11.4
1,494 9.3
7.4-11.5
1,502 8.8
7.0-11.1
0.182
0.670
F
1,046 7.5
5.8-9.6
1,177 8.3
6.6-10.3
980
3.9-7.0
977
4.6-8.3
2.982
0.085
M
309
16.2 11.8-21.9 164
7.6
4.2-13.5
236
18.4 13.0-25.5 182
18.6 13.4-25.3 1.126
0.289
F
206
12.4 8.4-17.8
11.3 6.8-18.4
155
6.0
2.7-12.6
114
8.7
4.7-15.4
2.498
0.115
Total
%
C.I.
n
C.I.
Wald F P-value Trend
3,020 7.2
6.0-8.5
3,028 7.4
%
6.1-8.9
2.206
7.7-10.9
1,818 8.6
7.0-10.5
1,807 8.6
7.0-10.5
0.259
0.611
6.7-10.1
1,202 5.0
3.8-6.5
1,221 5.6
4.3-7.4
5.338
0.021
9.9
6.5-14.6
342
308
5.2-12.2
0.293
0.589
179
8.6
5.0-14.4
170
293
14.9 10.4-20.9 366
0.138
Age
10.0 6.9-14.4
5.6
8.0
2.9-10.6
142
12.8 7.8-20.2
0.391
0.532
10.0 7.1-13.9
373
13.2 8.1-20.8
0.104
0.747
Province
Nairobi
Central
Coast
Eastern
Nyanza
Rift-Valley
Western
120
13.8 8.3-22.2
111
4.6
2.1-10.1
84
9.7
4.0-21.7
0.010
0.922
68
10.8 3.8-26.9
88
4.9
1.2-18.2
73
2.9
0.6-12.5
0.089
0.766
1.4-8.3
127
9.2
5.2-15.9
182
5.2
2.8-9.3
122
5.2
2.5-10.5
0.213
0.645
2.0-10.2
142
8.2
3.7-17.1
145
6.6
3.6-11.6
110
7.2
3.6-14.1
0.409
0.524
Residence
5.2
6.2
Maternal Education
No education
Incomplete primary
Complete primary
144
M
710
9.4
7.1-12.2
557
12.2 9.5-15.4
678
11.1 8.5-14.4
666
8.5
6.1-11.6
0.296
0.587
F
476
6.6
4.5-9.7
548
8.6
456
5.7
411
4.8
2.6-8.4
2.006
0.157
6.3-11.7
3.8-8.4
M
366
8.0
5.6-11.4
377
9.1
6.3-13.1
519
3.4
2.0-5.5
562
6.6
4.5-9.7
1.994
0.158
F
226
6.4
3.6-11.0
370
8.6
5.7-12.8
326
5.1
3.1-8.3
363
8.9
5.6-13.8
0.186
0.666
363
4.4
2.4-7.8
148
7.3
3.7-14.2
165
5.3
2.5-11.0
157
7.4
3.7-14.1
1.048
0.306
227
4.6
2.5-8.0
132
5.1
2.2-11.6
97
4.7
1.8-11.6
111
4.1
1.5-10.7
0.024
0.878
Incomplete secondary M
F
Matanda et al. BMC Pediatrics 2014, 14:5
http://www.biomedcentral.com/1471-2431/14/5
Page 5 of 13
Table 2 Wasting trends by age, province, residence, maternal education and wealth index, KDHS (Continued)
Secondary +
M
42
1.1
F
45
10.5 3.7-26.5
0.2-7.9
256
4.8
2.4-9.1
220
5.3
2.8-9.7
240
6.7
3.9-11.5
1.718
0.190
225
6.6
3.8-11.3
168
1.9
0.7-4.7
223
1.2
0.4-3.4
14.170
0.000
Poorest
M
403
15.2 11.3-20.2 378
11.3 8.1-15.6
442
10.7 7.2-15.7
433
13.0 9.7-17.2
0.601
0.439
F
279
7.6
4.5-12.4
345
9.3
6.5-13.1
263
6.9
4.2-11.2
264
7.6
4.5-12.6
0.086
0.769
Poorer
M
395
10.1 7.3-13.7
302
12.2 8.8-16.5
391
10.8 7.4-15.4
415
9.6
5.1-17.4
0.053
0.818
F
240
10.9 7.4-15.7
303
8.0
5.2-12.2
269
5.2
3.0-9.1
243
7.0
3.4-13.7
1.858
0.173
Middle
M
385
6.6
288
5.9
3.2-10.4
347
7.5
4.8-11.3
350
5.8
3.1-10.6
0.010
0.922
F
227
8.0
4.7-13.3
258
9.8
6.3-14.8
236
3.5
1.8-6.6
240
4.9
2.7-8.9
3.945
0.047
Richer
M
343
7.8
4.9-12.2
271
6.0
3.6-9.9
316
7.4
4.5-11.7
305
7.1
4.5-10.9
0.028
0.868
F
231
5.1
2.6-9.8
258
8.1
4.7-13.5
213
4.8
2.4-9.1
246
5.7
3.1-10.3
0.036
0.849
Richest
M
264
3.4
1.8-6.4
262
9.5
6.1-14.5
323
5.6
3.5-8.8
305
5.6
3.1-9.8
0.100
0.752
F
204
4.7
2.5-8.8
256
5.6
3.2-9.9
221
4.1
1.8-9.1
229
2.5
1.1-5.5
1.983
0.160
Wealth Index
4.4-9.6
C.I, 95% confidence intervals; Secondary +, complete secondary and/or higher education;
, significant decreasing trend.
National trends for boys and girls combined and for
boys aged 0–35 months showed no decline in wasting
across the study period (Table 2), while wasting did decrease significantly for girls from 7.3% in 1993 to 5.6% in
2008–09 (F(1, 1136) = 5.34, p < 0.021). The decline in
girls was concentrated in the age group 12–23 months
(F(1, 1046) = 8.98, p < 0.003), and the decline in boys was
concentrated in the same age group (F(1, 1046) = 5.71,
p < 0.017).
By province, a departure from the overall trends was
observed in Eastern and Nyanza provinces. In Eastern
province, wasting among boys decreased significantly
from 10.5% in 1993 to 4.8% in 2008–9 (F(1, 172) = 3.98,
p < 0.048). Boys in Nyanza province posted a significant
decline in wasting from 9.6% in 1993 to 6.1% in 2008–9
(F(1, 161) = 6.40, p < 0.012). Analyses by maternal education showed that the prevalence of wasting among girls
with mothers having complete secondary and/or higher
education declined significantly from 10.5% to 1.2% from
1993 to 2008–9 (F(1, 611) = 14.17, p < 0.000). Trends
by urban/rural residence were not statistically significant while those by WI showed girls in the middle
quintile decrease from 8.0% in 1993 to 4.9% in 2008–09
(F(1, 735) = 3.95, p < 0.047).
Comparing wasting prevalence between two survey
years (1993 versus 2008–09), boys recorded poor growth
patterns as compared to girls. Prevalence for boys increased among 6–11 months olds (10.4% to 13.2%), boys
in Rift-Valley increased (10.9% to 13.4%), and boys born
to mothers with no education (16.2% to 18.6%).
The gender-specific trends showed boys’ trend declining
from 41.7% in 1993 to 36.9% in 2008–9 (F(1, 1137) = 4.63,
p < 0.032) while the trend for girls was stable (Table 3).
There was a worsening trend in stunting for girls aged
12–23 months, with stunting increasing from 31.3% in
1993 to 40.1% in 2008–09 (F(1, 1044) = 4.18, p < 0.041).
However among girls aged 24–35 months, stunting declined significantly from 53.1% in 1993 to 43.1% in
2008–09 (F(1, 1017) = 9.88, p < 0.002). Analyses by province showed significant decreases in stunting prevalence
for boys in Nyanza from 40.6% in 1993 to 30.8% in
2008–09 (F(1, 162) = 5.35, p < 0.022).
The trends by maternal education were not significant for most sub-groups except a decline in stunting
among boys born to mothers with incomplete primary
education, from 48.8% in 1993 to 41.5% in 2008–09
(F(1, 956) = 5.05, p < 0.025). By WI, most trends were not
statistically significant, with the exception of a decline
among boys living in households in the richer WI quintile
(F(1, 717) = 5.98, p < 0.015).
While the overall national trend in stunting for boys and
girls combined stagnated during the study period, girls’
prevalence seemed to have gotten worse in certain sociodemographic segments comparing 1993 versus 2008–09.
Stunting prevalence was severe in 1993 and still increased
by 2008–09 among girls aged 12–23 months (31.3% to
40.1%), girls born to mothers with no education (42.9%
to 44.0%), girls born to mothers with complete primary
education (31.8% to 34.7%), and girls belonging to the
poorest (44.0% to 46.6%) and middle (34.4% to 39.6%),
wealth quintiles.
Trends in stunting
Trends in underweight
Nationally, prevalence in stunting for boys and girls
combined remained stagnant across the survey years.
Table 4 provides the detailed trend analysis for underweight. The national trend for all children and separate
Trends in wasting
Matanda et al. BMC Pediatrics 2014, 14:5
http://www.biomedcentral.com/1471-2431/14/5
Page 6 of 13
Table 3 Stunting trends by age, province, residence, maternal education and wealth index, KDHS
1993
Sex n
%
C.I.
1998
n
%
C.I.
2003
n
%
C.I.
2008-09
n
%
C.I.
Wald F P-value Trend
M/F 2,996 39.5 37.3-41.7 2,951 37.1 34.9-39.2 3,033 36.1 33.9-38.4 3,051 36.5 33.6-39.5 2.681
0.102
M
1,805 41.7 38.9-44.7 1,511 39.5 36.8-42.2 1,827 38.8 36.0-41.6 1,822 36.9 33.7-40.2 4.634
0.032
F
1,191 36.0 32.8-39.3 1,440 34.5 31.7-37.5 1,206 32.1 29.0-35.4 1,229 35.9 31.7-40.3 0.089
0.766
0-5 months
M
276
0.131
F
167
10.6 6.4-16.9
190
15.7 10.7-22.5 169
14.5 9.5-21.6
6-11 months
M
372
24.9 20.5-29.9 290
24.5 19.1-30.7 363
21.5 16.9-26.9 372
F
178
22.9 16.4-31.0 239
18.6 13.7-24.7 223
16.0 11.1-22.4 184
24.3 16.6-34.0 0.108
0.742
12-23 months
M
655
52.9 48.0-57.8 528
50.5 45.9-55.1 642
50.4 45.5-55.3 580
49.7 43.8-55.5 0.488
0.485
F
360
31.3 26.1-37.0 489
36.9 32.5-41.7 386
38.7 33.3-44.3 432
40.1 31.5-49.4 4.179
0.041
24-35 months
M
502
51.3 46.1-56.5 407
51.0 45.5-56.5 467
52.2 46.8-57.6 550
43.1 37.1-49.4 3.242
0.072
F
485
53.1 47.6-58.5 522
46.4 41.6-51.3 428
41.5 35.9-47.4 464
43.1 37.6-48.7 9.880
0.002
M
65
61.7 46.3-75.1 120
32.3 24.0-41.8 111
29.2 22.0-37.6 85
30.4 20.9-41.9 8.505
0.005
F
46
18.2 10.7-29.1 66
27.8 15.4-44.9 89
18.4 11.8-27.6 73
30.6 20.8-42.6 0.880
0.351
M
202
45.2 36.0-54.7 127
41.9 33.5-50.8 188
41.2 33.7-49.2 126
37.2 29.0-46.1 1.317
0.253
F
160
34.4 27.1-42.4 145
32.4 21.8-45.2 145
31.0 23.9-39.0 110
24.1 15.3-35.8 2.149
0.145
M
145
42.0 34.4-50.0 128
48.5 36.7-60.5 168
41.0 32.6-50.0 174
39.9 34.3-45.8 0.505
0.478
F
96
47.3 37.8-57.0 113
41.0 32.4-50.2 98
41.3 31.4-51.9 108
42.2 28.1-57.7 0.224
0.636
M
345
46.3 39.5-53.2 238
42.3 36.5-48.4 319
42.6 35.0-50.5 269
43.1 37.1-49.4 0.457
0.500
F
246
45.5 37.5-53.8 261
38.9 32.5-45.7 189
33.7 24.4-44.5 235
39.7 31.9-48.1 1.249
0.265
M
297
40.6 34.7-46.8 310
33.8 28.0-40.1 279
33.2 27.3-39.7 381
30.8 26.1-35.9 5.347
0.022
F
191
37.9 31.2-45.1 307
33.5 27.4-40.1 204
32.2 25.1-40.2 220
35.0 28.1-42.6 0.262
0.609
M
420
39.1 32.9-45.6 393
39.8 36.1-43.7 516
40.3 35.0-45.8 545
41.2 33.5-49.4 0.178
0.673
F
269
27.6 22.0-34.0 346
32.9 27.4-38.9 318
32.7 27.0-38.9 326
38.3 27.3-50.6 2.240
0.136
M
331
35.3 29.6-41.5 196
41.3 33.1-50.0 245
38.0 31.2-45.3 242
30.1 25.5-35.2 1.235
0.268
F
184
33.6 25.9-42.2 201
33.4 26.9-40.6 165
32.0 24.1-41.1 158
32.9 25.1-41.8 0.038
0.846
Urban
M
199
41.9 32.7-51.6 281
30.4 24.9-36.5 327
35.3 29.4-41.7 307
29.5 23.7-36.0 2.647
0.105
F
138
17.7 12.0-25.5 247
27.7 20.9-35.6 223
24.4 19.5-30.0 244
25.7 16.4-37.7 0.452
0.502
Rural
M
1,606 41.7 38.7-44.8 1,230 41.5 38.6-44.5 1,500 39.5 36.4-42.7 1,515 38.4 34.9-42.1 2.300
0.130
F
1,052 38.4 35.0-41.9 1,193 36.0 32.9-39.2 983
33.9 30.3-37.6 985
38.4 34.1-43.0 0.057
0.812
M
313
44.0 37.5-50.8 166
49.3 39.4-59.2 233
41.7 32.8-51.2 190
36.8 29.1-45.3 1.975
0.161
F
209
42.9 35.3-50.9 144
43.8 34.3-53.8 152
41.3 31.3-52.0 114
44.0 29.9-59.2 0.000
0.998
Total
Age
20.5 15.4-26.7 286
17.9 13.4-23.6 355
17.8 13.4-23.2 320
149
14.3 9.0-22.1
2.288
15.5 8.9-25.8
0.135
0.713
27.3 21.8-33.6 0.011
0.917
Province
Nairobi
Central
Coast
Eastern
Nyanza
Rift-Valley
Western
Residence
Maternal Education
No education
Incomplete primary
Complete primary
M
716
48.8 44.6-53.0 573
44.7 40.4-49.1 683
42.1 37.9-46.4 664
41.5 36.2-47.1 5.069
0.025
F
475
41.1 36.0-46.5 561
39.1 34.9-43.5 457
35.9 30.6-41.5 410
40.6 34.6-47.0 0.180
0.671
M
367
40.4 34.9-46.2 380
37.8 32.7-43.2 526
38.6 33.9-43.5 565
40.1 33.9-46.8 0.004
0.951
F
228
31.8 25.5-38.8 371
38.0 32.4-43.8 330
35.8 30.5-41.5 369
34.7 28.7-41.2 0.041
0.839
367
29.3 23.5-35.9 139
31.9 23.8-41.2 165
39.4 30.2-49.4 156
27.8 20.0-37.3 0.229
0.633
232
24.3 18.7-31.0 135
27.5 20.5-35.8 96
21.1 13.0-32.3 112
27.4 18.3-38.7 0.043
0.836
Incomplete secondary M
F
Matanda et al. BMC Pediatrics 2014, 14:5
http://www.biomedcentral.com/1471-2431/14/5
Page 7 of 13
Table 3 Stunting trends by age, province, residence, maternal education and wealth index, KDHS (Continued)
Secondary +
M
42
24.0 12.6-40.9 254
28.0 22.7-34.0 219
25.2 19.7-31.5 247
23.1 16.7-31.1 0.700
0.403
F
46
31.2 18.1-48.2 228
16.0 11.6-21.7 171
12.8 8.8-18.2
224
29.3 20.9-39.5 1.749
0.187
Poorest
M
405
48.6 42.8-54.5 384
50.6 44.5-56.6 448
45.8 40.9-50.8 450
42.9 36.8-49.3 2.568
0.110
F
281
44.0 37.8-50.5 353
44.0 38.1-50.1 261
40.4 33.4-47.9 268
46.6 39.8-53.4 0.066
0.797
Poorer
M
399
43.9 38.3-49.7 303
41.7 36.0-47.5 389
38.5 33.0-44.3 411
44.7 37.6-51.9 0.003
0.960
F
243
37.7 31.6-44.2 307
40.9 34.8-47.3 272
35.1 29.2-41.6 247
37.4 28.8-46.9 0.174
0.677
Middle
M
385
40.9 34.9-47.2 288
35.8 29.5-42.6 350
34.5 28.5-40.9 345
32.7 26.9-39.1 3.596
0.058
F
227
34.4 28.0-41.5 261
35.6 30.1-41.5 236
34.0 27.5-41.1 241
39.6 31.2-48.7 0.633
0.426
Richer
M
346
41.7 35.9-47.9 279
38.2 32.6-44.1 312
40.1 33.8-46.7 304
28.9 22.5-36.3 5.982
0.015
F
232
41.8 34.8-49.1 262
31.1 24.7-38.4 211
29.2 22.9-36.5 241
32.5 23.4-43.1 1.999
0.158
Richest
M
270
29.4 23.4-36.2 258
26.0 20.2-32.7 328
32.9 26.5-39.9 312
30.7 23.5-38.9 0.439
0.508
F
207
18.4 13.3-25.0 259
16.5 11.7-22.7 227
19.5 15.1-24.8 231
21.6 14.6-30.7 0.713
0.399
Wealth Index
C.I, 95% confidence intervals; Secondary +, complete secondary and/or higher education;
trends for boys and girls showed significant declines
in underweight. Underweight declined among boys
and girls combined, from 19.7% in 1993 to 15.0% in
2008–9 (F(1, 1136) = 11.80, p < 0.001), among boys from
21.4% in 1993 to 16.4% in 2008–09 (F(1, 1136) = 7.96,
p < 0.005), and among girls from 17.2% in 1993 to
12.8% in 2008–09 (F(1, 1136) = 7.24, p < 0.007). Age specific analysis showed significant declines among boys
aged 0–5 months (F(1, 932) = 9.37, p < 0.002), girls aged
6–11 months (F(1, 925) = 4.09, p < 0.043), and boys aged
12–23 months (F(1, 1048) = 8.32, p < 0.004).
Provincial analyses showed significant declines in underweight among boys and girls in Nyanza. Boys’ prevalence reduced from 21.6% in 1993 to 14.0% in 2008–09
(F(1, 161) = 6.95, p < 0.009) and that for girls reduced from
20.9% in 1993 to 10.8% in 2008–09 (F(1, 161) = 10.39,
p < 0.002). Boys and girls residing in rural areas recorded significant declines in underweight with boys’
levels reducing from 22.6% in 1993 to 17.1% in 2008–09
(F(1, 871) = 8.31, p < 0.004), and girls’ levels declining from
18.4% in 1993 to 13.8% in 2008–09 (F(1, 871) = 6.30,
p < 0.012).
Most of the trend analyses of maternal education were
not statistically significant. Only boys born to mothers
with incomplete primary education showed a significant decline from 26.1% in 1993 to 19.4% in 2008–09
(F(1, 967) = 7.44, p < 0.006). There was a significant declining trend in underweight among boys in the poorest
wealth quintile, from 31.7% in 1993 to 24.2% in 2008–
09 (F(1, 551) = 5.40, p < 0.020) and among girls in the
richer wealth quintile, from 15.2% in 1993 to 7.6% in
2008–09 (F(1, 716) = 4.26, p < 0.039). Comparison between the 1993 and 2008–09 surveys showed that
prevalence of underweight dropped in 2008–09 in almost all sub-groups.
, significant decreasing trend;
, significant increasing trend.
Discussion
For each survey year, the wasting prevalence estimate
was slightly lower for girls than for boys, which is consistent with previous studies from sub-Saharan Africa
[34,35]. The overall national trend for wasting showed
no significant change in the study period but there were
important differences in the trends by age and sex. Older
children aged 12–23 months showed a declining trend.
Evidence on child growth patterns from many countries
in the developing world shows that the prevalence of
wasting is stable at all measurement points from about
12 months of age and on, after a six month period of
sharply increasing wasting prevalence following weaning
[36]. Therefore, the lessened risk of wasting over time
observed in this study among Kenyan 12–23 month olds
may be a result of improved post-weaning child care and
feeding from the mid-1990’s on. This calls for closer
investigation of archival data from KDHS and other
sources on care and feeding patterns during the past two
decades, to observe which care and feeding factors and
trends may account for the reduction in wasting. The
emphasis on overall care, and not just feeding, is in concert with recent conclusions that proper hygiene practices and access to adequate water, proper sanitation and
reliable health services may be as important or even
more important determinants of child growth than feeding practices [37].
As to sex differences, wasting among girls overall declined significantly, while remaining stable among boys.
Yet some groups of boys did improve. Using a liberal
criterion for significance of p < 0.10, the pattern of significant trends in wasting (12 trends as shown in Table 2)
were all in the direction of improvement, observed
predominantly in females. But trends in wasting also
showed significant improvement among older boys and
Matanda et al. BMC Pediatrics 2014, 14:5
http://www.biomedcentral.com/1471-2431/14/5
Page 8 of 13
Table 4 Underweight trends by age, province, residence, maternal education and wealth index, KDHS
1993
Sex n
%
C.I.
1998
n
%
C.I.
2003
n
%
C.I.
2008-09
n
%
C.I.
Wald F P-value Trend
M/F 3,115 19.7 17.9-21.6 3,051 17.6 16.0-19.4 3,148 16.0 14.4-17.8 3,147 15.0 13.0-17.2 11.804
0.001
M
1,881 21.4 19.1-23.8 1,580 19.4 17.2-21.9 1,880 18.7 16.4-21.2 1,890 16.4 14.1-19.1 7.964
0.005
F
1,234 17.2 14.6-20.2 1,471 15.6 13.6-18.0 1,269 12.1 10.0-14.5 1,257 12.8 10.3-15.7 7.237
0.007
0-5 months
M
307
0.002
F
6-11 months
M
F
189
14.1 9.4-20.6
12-23 months
M
674
26.4 22.4-30.9 546
F
371
17.6 13.5-22.5 504
16.8 13.4-20.8 396
15.0 11.4-19.5 439
11.4 7.5-16.8
1.720
0.190
24-35 months
M
520
22.1 18.4-26.2 420
25.8 21.2-30.9 480
22.2 18.0-27.2 555
20.9 16.1-26.7 0.146
0.702
F
496
21.6 17.3-26.6 530
19.3 16.0-23.2 443
14.9 11.3-19.5 470
19.0 15.1-23.8 3.309
0.069
M
72
11.5 4.9-24.9
121
13.6 8.2-21.8
114
6.2
3.1-12.2
93
9.2
5.2-15.7
1.198
0.277
F
51
2.7
72
2.6
0.4-14.7
91
4.5
2.1-9.5
80
7.3
2.7-18.4
1.330
0.252
M
201
16.0 12.1-21.0 138
8.4
4.6-14.7
188
12.3 8.4-17.8
131
12.7 5.7-25.7
0.313
0.576
F
165
10.8 6.5-17.2
10.8 5.8-19.1
154
9.0
115
10.3 5.6-18.1
0.077
0.782
M
173
29.5 23.0-36.9 134
23.4 15.1-34.5 178
19.9 14.7-26.3 178
23.2 17.3-30.4 1.807
0.181
F
110
25.7 17.5-36.2 115
19.4 14.4-25.4 102
13.8 8.2-22.3
109
20.9 14.4-29.5 1.015
0.315
M
357
23.7 18.2-30.4 256
22.2 16.9-28.7 326
20.7 15.8-26.8 275
16.9 12.0-23.2 2.548
0.112
F
262
24.3 16.9-33.7 270
18.4 14.0-23.8 195
11.4 6.6-19.1
235
14.3 8.6-23.1
3.711
0.056
M
308
21.6 17.4-26.6 315
22.7 17.6-28.8 291
14.6 10.1-20.8 398
14.0 9.9-19.4
6.951
0.009
F
193
20.9 15.1-28.1 302
20.2 14.8-27.0 214
9.1
10.8 7.0-16.3
10.394
0.002
M
432
24.0 18.9-30.1 417
19.0 15.3-23.5 537
23.0 18.0-28.9 570
20.2 15.7-25.5 0.403
0.526
F
271
12.8 9.2-17.6
357
13.8 10.1-18.4 347
15.1 10.7-21.0 331
13.6 8.2-21.6
0.075
0.784
M
337
16.4 12.1-22.0 197
20.0 13.2-29.1 245
21.0 14.8-28.9 245
11.1 6.4-18.5
0.768
0.382
F
181
14.4 9.8-20.6
207
14.6 9.7-21.5
165
16.4 11.3-23.2 161
10.4 6.7-15.8
0.751
0.388
Urban
M
215
12.2 8.2-17.8
294
12.0 8.0-17.6
335
13.7 9.2-19.9
319
13.0 8.9-18.7
0.144
0.704
F
145
8.3
256
9.5
241
7.4
254
8.5
0.025
0.874
Rural
M
1,665 22.6 20.1-25.2 1,286 21.2 18.7-23.9 1,544 19.7 17.2-22.6 1,570 17.1 14.5-20.1 8.309
0.004
F
1,089 18.4 15.5-21.6 1,215 16.9 14.5-19.6 1,028 13.2 10.8-16.0 1,003 13.8 11.1-17.1 6.301
0.012
M
331
30.7 25.0-37.2 174
31.1 22.3-41.6 244
32.1 25.1-40.0 197
27.4 21.1-34.8 0.233
0.629
F
220
27.0 20.3-35.0 148
27.0 19.5-36.0 168
19.2 13.5-26.7 115
22.2 13.9-33.4 1.540
0.215
Total
Age
13.4 9.6-18.4
309
178
7.6
195
379
18.0 14.0-22.8 305
4.5-12.5
242
9.3
6.1-14.0
381
8.9
5.4-14.4
2,001 4.4
16.4 12.3-21.4 370
10.6 6.9-16.2
229
22.1 18.4-26.1 649
9.8
6.7-13.8
352
2.2-8.6
160
17.0 13.3-21.6 386
8.4
5.3-13.2
187
22.1 18.0-26.9 597
5.1
3.0-8.4
9.369
5.8
2.6-12.5
1.781
0.182
18.2 12.3-26.0 0.016
0.898
4.093
0.043
17.9 14.5-22.0 8.317
6.2
3.2-11.9
0.004
Province
Nairobi
Central
Coast
Eastern
Nyanza
Rift-Valley
Western
0.4-15.4
149
5.3-15.0
5.2-15.5
226
Residence
4.1-15.9
6.5-13.5
4.4-12.1
4.6-15.3
Maternal Education
No education
Incomplete primary
Complete primary
M
743
26.1 22.7-29.8 596
25.8 21.9-30.2 704
21.5 18.0-25.4 681
19.4 15.5-23.9 7.439
0.006
F
494
18.6 15.1-22.8 569
17.9 14.2-22.4 476
13.4 10.1-17.5 419
15.3 11.5-20.2 2.466
0.117
M
383
16.8 13.1-21.3 395
13.8 10.7-17.7 538
15.1 11.8-19.2 590
15.5 11.4-20.6 0.034
0.854
F
231
11.8 7.9-17.4
384
16.1 12.6-20.4 348
12.8 8.8-18.3
376
14.2 9.8-20.1
0.023
0.880
383
10.3 7.3-14.3
152
11.0 6.5-17.9
167
13.0 7.6-21.4
164
11.0 6.4-18.3
0.239
0.625
243
11.4 7.5-16.9
139
9.4
102
4.1
115
7.1
2.837
0.093
Incomplete secondary M
F
5.3-15.9
1.3-12.4
3.7-13.3
Matanda et al. BMC Pediatrics 2014, 14:5
http://www.biomedcentral.com/1471-2431/14/5
Page 9 of 13
Table 4 Underweight trends by age, province, residence, maternal education and wealth index, KDHS (Continued)
Secondary +
M
42
8.6
2.4-26.4
263
10.5 6.4-16.7
227
8.1
4.9-13.1
258
6.0
3.6-9.8
2.163
0.142
F
46
12.8 5.2-28.2
232
5.6
175
5.0
2.5-9.6
232
3.9
1.8-8.1
2.529
0.112
Poorest
M
419
31.7 26.3-37.5 398
24.2 19.3-29.8 5.402
0.020
F
290
22.6 17.4-28.7 353
22.0 17.6-27.1 277
18.4 13.2-25.1 271
16.9 12.4-22.6 2.718
0.100
Poorer
M
415
25.4 20.9-30.6 317
21.6 17.5-26.3 398
20.7 15.8-26.6 424
19.7 14.6-26.0 2.204
0.138
F
248
20.3 15.2-26.5 317
17.6 12.8-23.7 282
12.4 8.5-17.9
Middle
M
396
17.5 13.9-21.7 308
14.3 10.3-19.5 359
16.9 12.8-22.0 358
F
237
17.6 12.7-23.9 271
16.0 11.8-21.5 243
14.5 10.4-19.8 246
17.9 11.9-26.0 0.001
0.980
Richer
M
367
19.3 15.2-24.2 289
17.4 12.8-23.2 329
18.1 13.9-23.2 318
13.4 8.2-21.0
1.808
0.179
F
241
15.2 10.5-21.5 263
11.9 7.9-17.6
223
7.8
4.3-13.6
248
7.6
3.8-14.7
4.264
0.039
Richest
M
285
8.6
5.6-12.9
268
8.1
5.2-12.3
337
11.3 7.2-17.3
326
10.4 6.0-17.4
0.691
0.406
F
219
8.4
5.0-13.8
269
8.2
5.4-12.3
244
6.1
241
4.9
2.238
0.135
3.3-9.5
Wealth Index
30.9 25.6-36.7 457
C.I, 95% confidence intervals; Secondary +, complete secondary and higher education;
those living in Eastern and Nyanza provinces. The
favourable trends in these provinces for both girls and
boys are noteworthy, since Eastern province experiences
marked perennial food shortages, while Nyanza is among
the provinces with the highest poverty levels in Kenya
[38,39]. Climate research in the Eastern province has observed no discernible increasing or decreasing trend either in the annual or seasonal rainfall from 1960’s to the
present [40]. It seems unlikely that changing weather
conditions might have resulted in improved local food
production. In light of this, one possible explanation for
the improved wasting trends is the impact of food security initiatives, such as the Kenya Special Programme [39].
However, returning to the theme that overall care may be
as important as feeding care, evidence from many countries suggests the importance to child growth of policies
in diverse arenas. These include immunization, safe water
provision, female literacy, income distribution and support for agriculture [41]. Since it is unlikely that there is
any single source within countries with expertise and information on all these features of social and political life,
transdisciplinary research [42] seems essential to develop
better appreciation of the factors that underpin the trends
in child growth reported here.
Similar to wasting, trends in stunting at a national
level remained stagnant. However, stratification by sex
showed a decline among boys. The high prevalence in
stunting among boys as compared to girls is in agreement with the literature on stunting in sub-Saharan Africa
[11], but the improvement over time in boys, more so than
in girls, is difficult to explain. Looking to family dynamics,
the literature on parental sex bias in relation to child care
and feeding practices is contradictory and the evidence for
bias is scarce [12,13,35]. DHS data have been brought to
bear on this subject, but only via indirect inferences based
24.1 19.3-29.6 463
3.4-10.8
251
15.9 10.5-23.3 1.788
0.182
10.9 7.3-15.9
0.074
2.6-9.2
3.208
, significant decreasing trend.
on parental education differences [35]. Due to data limitations, the DHS, and most other survey data for that matter, may be inadequate for direct investigations of social
and psychological factors underlying sex differences in
child growth. Supporting this view is Marcoux’s meta
study of 306 child nutrition surveys from across the developing world, of which 74 percent showed no sex differences in wasting, stunting and underweight [43]. That sex
differences are difficult to detect reliably in survey research recommends against the use of the survey study
design in the search for factors underlying sex differences
in child growth. Mixed methods studies of cohorts, and of
cases and controls, may be more illuminating.
Analyses by age showed stunting to be relatively lower
in younger children and increased with age, in line with
other research evidence that the prevalence of stunting
increases with age [44]. The comparatively low and stable
prevalence posted by children in the youngest age category
(0–5 months) is likely due to stable childcare and feeding
practices during the pre-weaning stage of development.
Actually, in Kenya exclusive breastfeeding increased from
12.7% in 2003 to 31.9% in 2009, while early complimentary
feeding at the age of 2–3 months decreased from 81% in
1993 to 32% by 2008 [7]. That stunting in this age group
did not show a decline is likely due to a ‘floor effect’, with
near lowest feasible levels of stunting already achieved by
the mid-1990’s.
The high levels of stunting among children above
12 months and the increasing trend in stunting among
girls aged 12–23 months indicates the seriousness of
stunting, which seems to manifest itself at the onset of
complimentary feeding. Studies have shown that foods
used to compliment breastfeeding in Kenya are of low
nutritive value [45]. The most preferred porridge is made
of composite flours causing negative nutrient-nutrient
Matanda et al. BMC Pediatrics 2014, 14:5
http://www.biomedcentral.com/1471-2431/14/5
Page 10 of 13
Table 5 Growth and socio-demographic characteristics of the samples, KDHS
1993
1998
2003
2008-09
n
%
n
%
n
%
n
%
Wasted
249
8.4
255
8.7
216
7.2
224
7.4
Stunted
1,182
39.5
1,094
37.1
1,095
36.1
1,114
36.5
Underweight
615
19.7
537
17.6
504
16.0
471
15.0
Male
2,020
60.1
1,647
51.4
1,930
59.4
1,917
60.0
Female
1,343
39.9
1,559
48.6
1,320
40.6
1,281
40.0
0-5 months
521
15.5
523
16.3
603
18.6
516
16.1
6-11 months
593
17.6
564
17.6
615
18.9
585
18.3
12-23 months
1,124
33.4
1,097
34.2
1,080
33.2
1,048
32.8
24-35 months
1,126
33.5
1,021
31.9
952
29.3
1,049
32.8
Nairobi
157
4.7
213
6.7
216
6.7
181
5.6
Central
390
11.6
296
9.2
357
11.0
249
7.8
Growth
Sex
Age
Province
Coast
310
9.2
263
8.2
290
8.9
289
9.0
Eastern
668
19.9
546
17.0
537
16.5
514
16.1
Nyanza
526
15.6
641
20.0
510
15.7
630
19.7
Rift-Valley
769
22.9
824
25.7
924
28.4
927
29.0
Western
543
16.2
423
13.2
417
12.8
408
12.8
Urban
430
12.8
600
18.7
607
18.7
591
18.5
Rural
2,934
87.2
2,606
81.3
2,644
81.3
2,608
81.5
614
18.3
339
10.6
432
13.3
318
9.9
Residence
Maternal Education
No education
Incomplete primary
1,320
39.2
1,221
38.1
1,209
37.2
1,115
34.9
Complete primary
665
19.8
811
25.3
914
28.1
982
30.7
Incomplete secondary
670
19.9
306
9.6
281
8.6
283
8.8
Secondary +
94
2.8
528
16.5
416
12.8
501
15.7
Poorest
762
22.7
782
24.4
752
23.1
745
23.3
Poorer
702
20.9
663
20.7
695
21.4
684
21.4
Middle
672
20.0
606
18.9
620
19.1
610
19.1
Richer
657
19.5
581
18.1
571
17.6
576
18.0
Richest
570
16.9
573
17.9
614
18.9
584
18.3
Wealth Index
Secondary +, complete secondary and/or higher education.
interactions and also causing mal-absorption due to the
child’s immature gut. Such foods are also high in antinutrients such as phytates and tannins that bind available
nutrients and thus reduce bioavailability [45]. Further research is needed to explore the possibility that the nutritive value of the food served to girls in this age segment
has worsened over the study period. The significant
improvement among older children, especially among
girls aged 24–35 months, could be an indication of older
girls responding better to nutritional interventions leading
to catch up growth [36], but more research is needed to
investigate this issue.
The significant improvements in stunting levels in
Nairobi could be attributed to the accrued social-economic
Matanda et al. BMC Pediatrics 2014, 14:5
http://www.biomedcentral.com/1471-2431/14/5
and infrastructural advantage enjoyed in the capital region
in terms of the number of health facilities and personnel,
higher literacy levels and better economic performance
[46]. As in the case of wasting, the improvement in stunting among boys in Nyanza province was unexpected due
to its high incidence of poverty. Nevertheless, Nyanza
province has witnessed an increase in literacy levels [38]
and this could be one of the contributing factors to better
growth, as maternal literacy is associated with reduced risk
of stunting [47,48].
Higher socio-economic status is associated with better
utilization of health care services, better access to food
of high quality and quantity, better nutrition, improved
sanitation and household possessions [49,50]. This advantage was observed in the present study, with a significant reduction in stunting observed among children in
rich households but not in poor ones. The public health
significance of this pattern is alarming, even though Kenya
experienced a decrease in the percentage of people living
in poverty. It is estimated that the number of people living
below the poverty line increased from 13.4 million in 1997
to about 16.6 million in 2006 [51], increasing the number
of children in poor households and at risk of stunting.
Overall, the results show that faltering child growth remains a significant public health challenge in Kenya. It is
beyond the present scope to undertake an analysis of
causes of faltering growth in the Kenyan context. Here,
we must be content merely to point to the complexity of
the causal landscape, and the need for research that goes
beyond the simple descriptive analyses presented in this
paper. Among the critical causes of faltering child growth
are poor agricultural performance and food distribution at
a macro level, and micronutrient deficiency at the level of
individuals. At the macro level, there was a worrying decline in productivity in the Kenyan agricultural sector
from a real growth rate of 4.4% in 1996 to −5.4% in 2008.
This poor performance translates to less food for the fastgrowing Kenyan population, poor economic returns as a
result of a decline in agricultural export earnings, and increased unemployment due to the decrease in household
farm incomes [46,50,52].
At the individual level, dietary zinc in particular is essential in bolstering immunity, protein metabolism and
linear growth, and its deficiency precipitates retarded
growth [49]. Bwibo and Nuemann observed that food
served to Kenyan children has multiple micronutrient
deficiencies, placing child at risk of poor growth regardless of the quantity of food provided by the agricultural
sector [45].
This span from macro to micro level causal factors illustrates the complexity of the causal web that underlies
faltering child growth, to which an array of sub-optimal
childcare practices and inadequate access to health care
also contribute. This complexity is signaled strongly in
Page 11 of 13
the present findings that compare child growth trends in
urban and in rural areas. While the terms ‘urban’ and
‘rural’ are demographic concepts referring to population
number and density, urban versus rural living conditions
include important variation in social factors, such as
rates of unemployment and illiteracy, access to health facilities, and household and community poverty level
[53]. The declining trends in underweight in rural areas
as compared to stagnant trends in urban areas underscore the possibility that urban areas are experiencing a
decline in their perceived advantage over rural areas
[54]. The high urbanization rate brought about by rural–
urban migration has significantly reduced the infrastructural advantage urban areas used to enjoy, and has resulted
in increased urban poverty. While hardcore poverty declined in rural areas from 34.8% in 1997 to 21.9% in 2005/
06, it increased marginally in urban areas from 7.6% in
1997 to 8.3% in 2005/06 [46]. Further research on urban/
rural child growth patterns should therefore be complemented by studies of changing urban and rural living conditions, so that the context of child growth is better
appreciated. This should include differentiation between
urban areas generally and those in capital regions such as
Nairobi, which may enjoy special advantages due to proximity to central government.
Study limitations
The more data points over time the more robust the
trend analysis. With just the four data points (1993,
1998, 2003 and 2008–09) available for the present analyses, we treat our findings and interpretations with due
caution. However, we are not aware of any other data on
child undernutrition in Kenya with more than four data
points over time, and therefore consider the present effort defensible in the interest of providing the best trend
estimates possible with the limited data now available.
While decomposed analysis enables detailed understanding of trends within socio-demographic groups, it results
in reduction in sample size. Trends in certain groups may
fail to reach statistical significance, not necessarily due to
lack of changes in prevalence, but rather to limited sample
size giving rise to wider confidence intervals around prevalence estimates. It is not only relatively small sample size,
but also sample size variation, that may hinder comparison
of trends across socio-demographic groups. For example,
two sub-group trend analyses with identical prevalence estimates at four points in time may be judged statistically
significant in the sub-group with a relatively large n and insignificant in the sub-group with a relatively smaller n. In
surveys wherein the sampling design has not included
sampling strata at the level of socio-demographic subgroups, such as the DHS, the limitations associated with
variable n that were encountered in this study cannot be
overcome.
Matanda et al. BMC Pediatrics 2014, 14:5
http://www.biomedcentral.com/1471-2431/14/5
An alternative to the decomposition approach (stratification) we have taken is to use multivariate analysis to
control statistically for population composition changes
over time, and to control for confounding and for effect
modification (interactions amongst risk and protective
factors). While statistically elegant, the main value of
such multivariate analyses is to produce equations that
predict future changes in outcome variables as a function
of hypothesized changes in risk and protective factors.
This approach has less utility for policy work than the decomposition approach, which produces more easily digestible information about sub-group trends. Nevertheless, it
is a limitation of the present study that the multivariate relationships among the factors defining socio-demographic
sub-groups have not been taken into account in the analyses of undernutrition prevalence.
Conclusions
The national trends in childhood undernutrition in Kenya
showed significant declines in underweight, but trends in
wasting and stunting were stagnant. Analyses disaggregated by demographic and socio-economic segments revealed some departures from the overall trends. There
were more declines in wasting among girls than boys in
the various socio-demographic stratifications studied, and
the opposite was true for stunting, with boys posting more
declining trends compared to girls. These findings support
the importance of conducting trend analyses at disaggregated levels within countries, if findings are to be useful in
informing public health policy and the development of
better-targeted childcare interventions. Concerted efforts
should be made by relevant stakeholders to reduce the
stagnating trends of undernutrition, especially for stunting, which has consistently remained high in most sociodemographic segments in Kenya.
Endnotes
a
Since the promulgation of the new constitution in
2010, provinces were renamed as regions.
b
The KDHS is one of the MEASURE DHS projects
in developing countries that collect data on important
health indicators. It is a collaboration involving the Kenya
National Bureau of Statistics, National AIDS Control
Council, Ministry of Public Health and Sanitation, Kenya
Medical Research Institute, National Coordinating Agency
for Population and Development, ICF Macro, The United
States Agency for International Development (USAID) and
other non-governmental organizations.
Abbreviations
WI: Wealth index; KDHS: Kenya demographic and health survey;
SPSS: Statistical package for the social sciences; SD: Standard deviations;
WHZ: Weight-for-height Z-score; WLZ: Weight-for-length Z-score;
HAZ: Height-for-age Z-score; LAZ: Length-for-age Z-score; WAZ: Weight-forage Z-score; DHS: Demographic and health survey.
Page 12 of 13
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
DJM and MBM conceived the study, DJM conducted data analysis, interpretation
of results and drafting of the manuscript, and MBM and KDMD revised the
manuscript. All authors read and approved the final manuscript.
Acknowledgement
Discussions with Dickson A. Amugsi and Helga B. Urke were of great value
throughout the process of planning and completing this paper. We are
grateful for helpful consultations early in the writing process with Professor
Anna Lartey and during the analysis phase with Professor Stein Atle Lie.
Author details
1
Department of Health Promotion and Development, University of Bergen,
P.O.Box 7807, NO-5020, Christiesgt. 13 Bergen, Norway. 2Department of
Foods, Nutrition and Dietetics, Kenyatta University, Nairobi, Kenya.
Received: 7 February 2013 Accepted: 7 January 2014
Published: 13 January 2014
References
1. Black RE, Allen LH, Bhutta ZA, Caulfield LE, de Onis M, Ezzati M, et al:
Maternal and child undernutrition: global and regional exposures and
health consequences. Lancet 2008, 371(9608):243–260.
2. de Onis M, Blössner M, Borghi E: Prevalence and trends of stunting
among pre-school children, 1990–2020. Public Health Nutr 2011, 1(1):1–7.
3. de Onis M: Measuring nutritional status in relation to mortality. Bull World
Health Org 2000, 78(10):1271–1274.
4. Rivera JA, Hotz C, González-Cossío T, Neufeld L, García-Guerra A: The effect
of micronutrient deficiencies on child growth: a review of results from
community-based supplementation trials. J Nutr 2003, 133(11):4010S–4020S.
5. Black RE, Morris SS, Bryce J: Where and why are 10 million children dying
every year? Lancet 2003, 361(9376):2226–2234.
6. Bryce J, Terreri N, Victora CG, Mason E, Daelmans B, Bhutta ZA, et al:
Countdown to 2015: tracking intervention coverage for child survival.
Lancet 2006, 368(9541):1067–1076.
7. Kenya National Bureau of Statistics (KNBS) and ICF Macro: Kenya Demographic
and Health Survey, 2008–09. Calverton, Maryland: KNBS and ICF Macro; 2010.
8. World Health Organization-Regional office for Africa: Atlas of African Health
Statistics 2012 - Health situation analysis of the African Region. 2012
[cited 2012 Nov 8]. Available from: [http://www.aho.afro.who.int/en/publication/
63/atlas-african-health-statistics-2012-health-situation-analysis-african-region]
9. de Onis M, Blössner M: WHO Global Database on Child Growth and
Malnutrition. Geneva: World Health Organization; 1997.
10. Shetty P: Malnutrition and undernutrition. Medicine 2006, 34(12):524–529.
11. Wamani H, Åstrøm AN, Peterson S, Tumwine JK, Tylleskär T: Boys are more
stunted than girls in sub-Saharan Africa: a meta-analysis of 16 demographic
and health surveys. BMC Pediatr 2007, 7(1):17.
12. Crognier E, Baali A, Hilali M-K, Villena M, Vargas E: Preference for sons and
sex ratio in two non-Western societies. Am J Hum Biol 2006, 18(3):325–334.
13. Cronk L: Low socioeconomic status and female-biased parental investment:
the mukogodo example. Am Anthropol 1989, 91(2):414–429.
14. Hill K, Upchurch DM: Gender differences in child health: evidence from
the demographic and health surveys. Popul Dev Rev 1995, 21(1):127–151.
15. O’Donnell O, van Doorslaer E, Wagstaff A, Lindelow M: Analyzing Health
Equity Using Household Survey Data: A Guide to Techniques and Their
Implementation. Washington DC: The World Bank; 2008.
16. Organization PAH: World Health Organization. Complementary Feeding:
Report of the Global Consultation and Summary of Guiding Principles for
Complementary Feeding of the Breastfed Child. Washington DC: Pan
American Health Organization, WHO; 2002.
17. Adeladza A: The Influence of Socio-Economic and Nutritional Characteristics
on Child Growth in Kwale District of Kenya. Agriculture, Nutrition and
Development: African Journal of Food; 2009. 9(7).
18. Vaahtera M, Kulmala T, Hietanen A, Ndekha M, Cullinan T, Salin M, et al:
Breastfeeding and complementary feeding practices in rural Malawi.
Acta Paediatr 2001, 90(3):328–332.
Matanda et al. BMC Pediatrics 2014, 14:5
http://www.biomedcentral.com/1471-2431/14/5
19. Bhutta ZA, Ahmed T, Black RE, Cousens S, Dewey K, Giugliani E, et al: What
works? Interventions for maternal and child undernutrition and survival.
Lancet 2008, 371(9610):417–440.
20. Frost MB, Forste R, Haas DW: Maternal education and child nutritional
status in Bolivia: finding the links. Soc Sci Med 2005, 60(2):395–407.
21. Rabiee F, Geissler C: Causes of malnutrition in young children: Gilan, Iran.
J Trop Pediatr 1990, 36(4):165–170.
22. Bloss E, Wainaina F, Bailey RC: Prevalence and predictors of underweight,
stunting, and wasting among children aged 5 and under in western
Kenya. J Trop Pediatr 2004, 50(5):260–270.
23. Friedman JF, Kwena AM, Mirel LB, Kariuki SK, Terlouw DJ, Phillips-Howard
PA, et al: Malaria and nutritional status among pre-school children:
results from cross-sectional surveys in western Kenya. Am J Trop Med Hyg
2005, 73(4):698–704.
24. Olack B, Burke H, Cosmas L, Bamrah S, Dooling K, Feikin DR: Nutritional
status of under-five children living in an informal urban settlement in
Nairobi, Kenya. J Health Popul Nutr 2011, 29(4):357.
25. Abuya BA, Ciera JM, Kimani-Murage E: Effect of mother’s education on
child’s nutritional status in the slums of Nairobi. BMC Pediatr 2012,
12(1):80.
26. MEASURE DHS, ICF International, Calverton. 2013. Available from:
[http://www.measuredhs.com/data/Access-Instructions.cfm]
27. National Council for Population and Development (NCPD), Central Bureau
of Statistics (CBS) (Office of the Vice President and Ministry of Planning and
National Development, Kenya), and Macro International Inc. (MI): Kenya
Demograhic and Health Survey, 1993. Calverton, Maryland: NCPD, CBS,
and MI; 1994.
28. National Council for Population and Development (NCPD), Central Bureau
of Statistics (CBS) (Office of the Vice President and Ministry of Planning and
National Development, Kenya), and Macro International Inc. (MI): Kenya
Demographic and Health Survey, 1998. Calverton, Maryland: NCPD, CBS,
and MI; 1999.
29. Central Bureau of Statistics (CBS) Kenya, Ministry of Health (MOH) Kenya,
and ORC Macro: Kenya Demographic and Health Survey 2003. Calverton,
Maryland: CBS, MOH, and ORC Macro; 2004.
30. Rutstein SO, Rojas G: Guide to DHS statistics. Maryland: Demographic and
Health Surveys ORC Macro Calverton; 2006.
31. Rutstein SO, Johnson K: The DHS Wealth Index (DHS Comparative Reports No. 6).
ORC Macro.: Calverton; 2004.
32. World Health Organization: WHO Child Growth Standards: Methods and
Development: Length/Height-for-age, Weight-for-age, Weight-for-Length,
Weight-for-Height and Body Mass Index-for-age. Geneva: WHO; 2006.
33. World Health Organization: Physical status: the use and interpretation of
anthropometry. Report of a WHO expert committee. Technical report
series No. 854. 1995 [cited 2012 Sep 28]. Available from: [http://www.who.
int/childgrowth/publications/physical_status/en/index.html]
34. Svedberg P: Undernutrition in Sub-Saharan Africa: is there a gender bias?
J Dev Stud 1990, 26(3):469–486.
35. Sahn DE, Stifel DC: Parental preferences for nutrition of boys and girls:
evidence from Africa. J Dev Stud 2002, 39(1):21–45.
36. Shrimpton R, Victora CG, de Onis M, Lima RC, Blössner M, Clugston G:
Worldwide timing of growth faltering: implications for nutritional
interventions. Pediatrics 2001, 107(5):e75.
37. Jones AD, Ickes SB, Smith LE, Mbuya MN, Chasekwa B, Heidkamp RA: World
Health Organization infant and young child feeding indicators and their
associations with child anthropometry: a synthesis of recent findings.
Matern Child Nutr 2014, 10:1–17.
38. Kenya National Bureau of Statistics, Ministry of Planning and National
Development: Kenya Integrated Household Budget Survey (KIHBS) 2005/06
(Revised Edition). Nairobi: Kenya National Bureau of Statistics, Ministry of
Planning and National Development; 2006.
39. Odumbe M: Food Security District Profiles. Nairobi: FAO; 2007.
40. Rao KPC, Ndegwa WG, Kizito K, Oyoo A: Climate variability and change:
farmer perceptions and understanding of intra-seasonal variability
in rainfall and associated risk in semi-arid Kenya. Exp Agric 2011,
47(2):267–291.
41. Milman A, Frongillo EA, de Onis M, Hwang J-Y: Differential improvement
among countries in child stunting is associated with long-term development
and specific interventions. J Nutr 2005, 135(6):1415–1422.
42. Angelstam P, Andersson K, Annerstedt M, Axelsson R, Elbakidze M,
Garrido P, et al: Solving problems in social–ecological systems:
Page 13 of 13
43.
44.
45.
46.
47.
48.
49.
50.
51.
52.
53.
54.
definition, practice and barriers of transdisciplinary research. Ambio 2013,
42(2):254–265.
Marcoux A: Sex differentials in undernutrition: a look at survey evidence.
Popul Dev Rev 2002, 28(2):275–284.
Nyovani JM, Matthews Z, Margetts B: Heterogeneity of child nutritional
status between households: a comparison of six sub-Saharan African
countries. Pop Stud 1999, 53(3):331–343.
Bwibo NO, Neumann CG: The need for animal source foods by Kenyan
children. J Nutr 2003, 133(11):3936S–3940S.
Kenya Institute for Public Policy Research and Analysis: Kenya Economic
Report 2009. Nairobi: Kenya Institute for Public Policy Research and
Analysis; 2009.
Borooah V: Maternal literacy and child malnutrition in India. 2009 [cited
2013 Nov 28]; Available from: [http://mpra.ub.uni-muenchen.de/19833/]
Frongillo EA, de Onis M, Hanson KM: Socioeconomic and demographic
factors are associated with worldwide patterns of stunting and wasting
of children. J Nutr 1997, 127(12):2302–2309.
Miller JE, Rodgers YV: Mother’s Education and children’s nutritional status:
New evidence from Cambodia. Asian Dev Rev 2009, 26:131–132.
United Nations Children’s Fund (UNICEF): Government of Kenya. 2009
Situation Analysis of Children, Young People and Women in Kenya. Nairobi:
UNICEF, Government of Kenya; 2009.
Government of Kenya, Ministry of Planning and National Development:
Kenya Economic Recovery Strategy for Wealth and Employment Creation
2003–2007. Nairob: Government of Kenya, Ministry of Planning and National
Development; 2003.
Government of Kenya, Kenya National Bureau of Statistics: 2009 Kenya
Population and Housing Census Volume 11, Population and Household
Distribution by Socio-Economic Characteristics. Nairobi: Government of
Kenya, Kenya National Bureau of Statistics; 2009.
United Nations Development Programme (UNDP): Kenya National Human
Development Report 2006. New York: UNDP; 2006.
Haddad L, Ruel MT, Garrett JL: Are urban poverty and undernutrition
growing? Some newly assembled evidence. World Dev 1999,
27(11):1891–1904.
doi:10.1186/1471-2431-14-5
Cite this article as: Matanda et al.: Child undernutrition in Kenya: trend
analyses from 1993 to 2008–09. BMC Pediatrics 2014 14:5.
Submit your next manuscript to BioMed Central
and take full advantage of:
• Convenient online submission
• Thorough peer review
• No space constraints or color figure charges
• Immediate publication on acceptance
• Inclusion in PubMed, CAS, Scopus and Google Scholar
• Research which is freely available for redistribution
Submit your manuscript at
www.biomedcentral.com/submit