Community and International Nutrition
Dietary Diversity Is Associated with Child Nutritional Status:
Evidence from 11 Demographic and Health Surveys1,2
Mary Arimond3 and Marie T. Ruel
Food Consumption and Nutrition Division, International Food Policy Research Institute (IFPRI),
Washington, DC 20006
KEY WORDS: ● Demographic and Health Surveys (DHS)
● diet quality ● socioeconomic factors
All people need a variety of foods to meet requirements for
essential nutrients, and the value of a diverse diet has long
been recognized. Lack of diversity is a particularly severe
problem among poor populations in the developing world,
where diets are based predominantly on starchy staples and
often include few or no animal products and only seasonal
fruits and vegetables. For vulnerable infants and young children, the problem is particularly critical because they need
energy- and nutrient-dense foods to grow and develop both
physically and mentally and to live a healthy life. For these
reasons, dietary diversity is now included as a specific recommendation in the recently updated guidance for complementary feeding of the breast-fed child aged 6 to 23 mo (1).
Because of the perceived importance of dietary diversity for
health and nutrition, indicators of dietary diversity have become increasingly popular in recent years. These types of
●
dietary diversity
●
child nutritional status
indicators are particularly attractive because they are relatively
simple to measure and they are thought to reflect nutrient
adequacy, i.e., individuals consuming more diverse diets are
thought to be more likely to meet their nutrient needs. Simple
yet valid indicators are of particular importance for large
household surveys and for program management.
In developed countries, there are a number of studies linking dietary diversity to nutrient intake, particularly among
adults; these studies are reviewed by Kant (2). Although there
is some indication from the literature that dietary diversity is
positively associated with a greater intake of energy and several other nutrients among young children in developing
countries (3– 6), additional research is warranted to characterize the exact nature of the relation between dietary diversity
and nutrient intake and adequacy. In young children, dietary
diversity has also been associated with improved nutritional
status (4,7–9), suggesting that diversity may indeed reflect
higher dietary quality and greater likelihood of meeting daily
energy and nutrient requirements.
However, dietary diversity was also shown to be strongly
associated with household socioeconomic status (8,10), and
links between socioeconomic status and child nutrition and
health outcomes have long been established. Interpretation of
associations between dietary diversity and nutritional status is
therefore complicated by the fact that both are strongly linked
1
Preliminary results were reported in Proceedings of the 2nd International
Workshop, Ouagadougou, November 23–28, 2003 [Ruel, M. T. & Arimond, M.
(2004) Dietary diversity and growth: an analysis of recent demographic and
health surveys. In: Food Based Approaches for a Healthy Nutrition in West Africa
(Brower, E. D., Traore, A. S. & Treche, S., eds.). University Press, Ouagadougou
(in press)].
2
Funded in part by the Food and Nutrition Technical Assistance Project
(FANTA) managed by the Academy for Educational Development for USAID.
3
To whom correspondence should be addressed.
E-mail: m.arimond@cgiar.org.
0022-3166/04 $8.00 © 2004 American Society for Nutritional Sciences.
Manuscript received 2 June 2004. Initial review completed 1 July 2004. Revision accepted 2 August 2004.
2579
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ABSTRACT Simple indicators reflecting diet quality for young children are needed both for programs and in some
research contexts. Measures of dietary diversity are relatively simple and were shown to be associated with nutrient
adequacy and nutritional status. However, dietary diversity also tends to increase with income and wealth; thus,
the association between dietary diversity and child nutrition may be confounded by socioeconomic factors. We
used data from 11 recent Demographic and Health Surveys (DHS) to examine the association between dietary
diversity and height-for-age Z-scores (HAZ) for children 6 –23 mo old, while controlling for household wealth/
welfare and several other potentially confounding factors. Bivariate associations between dietary diversity and HAZ
were observed in 9 of the 11 countries. Dietary diversity remained significant as a main effect in 7 countries in
multivariate models, and interacted significantly with other factors (e.g., child age, breast-feeding status, urban/
rural location) in 3 of the 4 remaining countries. Thus, dietary diversity was significantly associated with HAZ, either
as a main effect or in an interaction, in all but one of the countries analyzed. These findings suggest that there is
an association between child dietary diversity and nutritional status that is independent of socioeconomic factors,
and that dietary diversity may indeed reflect diet quality. Before dietary diversity can be recommended for
widespread use as an indicator of diet quality, additional research is required to confirm and clarify relations
between various dietary diversity indicators and nutrient intake, adequacy, and density, for children with differing
dietary patterns. J. Nutr. 134: 2579 –2585, 2004.
ARIMOND AND RUEL
2580
SUBJECTS AND METHODS
Data
Data from recent DHS surveys from 11 countries were used. The
DHS are a series of standardized, nationally representative surveys
that have been implemented in ⬃70 countries since 1984. The
selection criteria for the countries included in the analyses were the
following: 1) Data set was available in mid-2002 and used the general
format of the most recent “MEASURE DHS⫹” questionnaire; 2) The
country was from the African, South or Southeast Asian, or the Latin
America/Caribbean (LAC) region; 3) At least 6 of 7 broad food
groups needed to create the diversity indicator (see description below) were represented in the questionnaire. Eleven data sets met
these criteria: Benin (2001), Cambodia (2000), Colombia (2000),
Ethiopia (2000), Haiti (2000), Malawi (2000), Mali (2001), Nepal
(2001), Peru (2000), Rwanda (2000), and Zimbabwe (1999).5
All of the DHS that follow a standard protocol were given blanket
approval by the ORC Macro Institutional Review Board. Every
survey that deviated substantially from the standard protocol was
reviewed and approved separately. Each survey also received approval
from an in-country ethical review board, if such an organization
existed (personal communication, Altrena Mukuria, ORC Macro,
International).
Samples
After excluding children for whom age information was missing,
we randomly selected 1 child ⬍ 2 y of age in each household. The
proportion of children with missing values for age ranged from 0% in
3 countries (Ethiopia, Colombia, and Peru) to 8% in Zimbabwe.
Sample sizes for children aged 6 –23 mo ranged from 958 in Zimbabwe
to 3662 in Peru. A number of children were missing anthropometric
4
Abbreviations used: DHS, Demographic and Health Surveys [The DHS
program is funded by the U.S. Agency for International Development (USAID) and
administered by ORC Macro. ORC Macro provides technical assistance to partner institutions in each country.]; HAZ, height-for-age Z-score(s); LAC, Latin
America/Caribbean; VIF, variance inflation factor; WHZ, weight-for height Zscore(s).
5
At the time data were accessed, data from Ethiopia, Haiti, Mali, Nepal, and
Peru were indicated to be “preliminary data” on ORC Macro website.
measurements or had unacceptably extreme values. The proportion of
children with missing or extreme anthropometric values ranged from
2% in Nepal to 20% in Zimbabwe; these children were excluded from
bivariate and multivariate analyses.
Variable creation
Dietary diversity. The dietary diversity indicator used in the
analysis was created using data from the 7-d recall of foods/food
groups available in the MEASURE DHS⫹ surveys.6 Our general
approach was to develop a score that included a point for each of the
major nutritionally important types of food the child may have eaten,
while providing some balance between plant foods and animal-source
foods. Therefore, for the purpose of our analysis, foods/food groups
were regrouped and summed into a 7-point dietary diversity score, as
follows:7 1) starchy staples (foods made from grain, roots, or tubers);
2) legumes; 3) dairy (milk other than breast milk, cheese, or yogurt);
4) meat, poultry, fish, or eggs; 5) vitamin A-rich fruits and vegetables
(pumpkin; red or yellow yams or squash; carrots or red sweet potatoes;
green leafy vegetables; fruits such as mango, papaya, or other local
vitamin A-rich fruits); 6) other fruits and vegetables (or fruit juices);
and 7) foods made with oil, fat, or butter. Foods/food groups that the
child had consumed on ⱖ3 d in the previous week received a score of
“1” and those that the child had consumed ⬍3 times in the past week
were scored “0.”8 The choice of “ⱖ3 d” was arbitrary but was meant
to capture foods eaten regularly.
Terciles of dietary diversity were used to classify children into low,
average, and high diversity. The terciles were derived separately for
each country, and were made age-specific within the following age
ranges: 6 – 8 mo, 9 –11 mo, 12–17 mo, and 18 –23 mo. The rationale
for using age-specific terciles is that diversity increases rapidly with
age; by using age-specific terciles, children were ranked as having low,
average, or high diversity compared with other children in their age
range. For example, in Malawi, the high diversity tercile included
infants 6 – 8 mo who ate 2 or more food groups, those 9 –11 mo who
ate 3 or more, and those 12–23 mo who ate 4 or more. Countryspecific terciles were used because there are currently no international
guidelines or recommendations on which to base cutoffs for “low” or
“high” diversity. Tercile cutoffs were lowest in Mali and Ethiopia, and
highest in the LAC region across all age groups.
Maternal and child nutritional status. Height-for-age Z-scores
(HAZ) were used as an indicator of nutritional status, and maternal
height and BMI were used for maternal nutritional status; extreme
values were excluded (11).
Proxies for household wealth and welfare. A variety of approaches have been used to characterize household wealth, welfare,
and socioeconomic status, including measurement of income and
expenditures and approaches incorporating information about household assets and access to services (12). Recently, authors analyzing
DHS and other similar surveys developed indices using information
on household assets, water and sanitation, and services (13,14). We
used a similar approach, with factor analysis as a data reduction tool,
to combine a large number of household-level variables into several
factors, with the objective of constructing a proxy for household
wealth and welfare. Categories of variables included in the factor
analysis (when available) were as follows: ownership of household
assets (radios, telephones, television, refrigerator), productive assets
(agricultural implements, land, sewing machines, bicycles, boats),
animals; main source of drinking water; type of sanitation facility;
main material of the floor and of the roof; and crowding (number of
household members per sleeping room).
Factor analysis was done separately for each country; some categories of variables were not available in all countries. Variables were
entered into the factor analysis either as summed scores or as ordered
variables with increasing scores reflecting increasing quality. For
6
In Haiti, the 24-h food group recall was used to construct the dietary
diversity variable, because the Haitian questionnaire did not include a 7-d recall.
7
Ten of the 11 countries included all 7 food groups. In Zimbabwe, the food
group list did not include foods made with fats and oils.
8
In Haiti, children received a score of “1” if they had the food yesterday, and
“0” if not.
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to household socioeconomic factors. Families with greater
incomes and resources tend to have more diverse diets, but
they are also likely to have better access to health care, and
better environmental conditions. Clearly, children in wealthier households are better off and grow better for a number of
reasons, but improved nutrient adequacy may be one important way in which household wealth and resources translate
into better outcomes for children. Thus, a key question is
whether dietary diversity is independently associated with
better child nutritional status because it accurately reflects
nutrient adequacy, or whether the association is found primarily because dietary diversity is a particularly good proxy for
household socioeconomic status.
The present study addresses this question using data from
recent Demographic and Health Surveys (DHS)4 from 11
countries. Our focus is on infants and young children 6 –23 mo
of age, during the vulnerable period of transition from breastfeeding to the family diet. The overall goal of the research was
to determine whether an association between child dietary
diversity and nutritional status among 6- to 23-mo-old children was found across countries and regions with varying
dietary patterns, and whether this association remained once
socioeconomic factors were controlled for by multivariate
analyses. Answers to these questions are key to understanding
the nature of the links between dietary diversity and child
outcomes, and to fostering progress in developing simple indicators of dietary quality.
DIETARY DIVERSITY AND CHILD NUTRITIONAL STATUS
example, household assets, productive assets, and animals were each
summed, with items scored “1” if present, “0” if not. Water source,
sanitation facilities, and floor and roof materials were scored from
lowest to highest quality.
The factors were derived separately for urban and rural areas,
because the assets and household characteristics that differentiate
better off from worse off households in urban and rural areas are likely
to differ. After initial exploration, all models were restricted to 2
factors that, taken together, explained from 47 to 68% of the shared
variability in urban areas, and from 33 to 62% of the shared variability in rural areas. In most cases, retaining 2 factors was equivalent to
retaining all factors with initial eigenvalues ⬎ 1. Scores for the 2
factors were used as continuous variables in the models.
Analytical methods
nal [height, BMI, education and number of prenatal care visits (a
proxy for access to health care)], and household level (wealth/welfare
factors 1 and 2, urban/rural location, and number of children ⬍ 5 y
old). Least-square means (adjusted for continuous variables in the
model) were computed to assess the difference in HAZ by dietary
diversity terciles. Multicollinearity was assessed in the models using
the variance inflation factor (VIF) (15); only age and age squared
showed evidence of multicollinearity (VIF ⬎ 10). Removing age
squared from the models did not change results for dietary diversity;
thus, age squared was retained in the models for theoretical reasons.
Two-way interactions between dietary diversity and several factors
were also tested in the multivariate analyses because we hypothesized
that the association between diversity and child nutritional status
might vary depending on certain child, maternal, or household characteristics, i.e., we tested the two-way interactions between dietary
diversity and the following plausible factors: child age, whether child
was still breast-fed, mother’s education, urban/rural location, and
wealth/welfare factors. Main effects and interactions were considered
significant at P-values ⬍ 0.05. For categorical variables, statistical
significance was assessed with joint tests of main effects.
RESULTS
Characteristics of survey households, mothers,
and children
Key descriptive statistics for the survey households, mothers
and children highlight some of the major differences between
countries (Table 1). In most countries, more than two-thirds
of the households lived in rural areas; in Colombia and Peru,
TABLE 1
Selected household, maternal, and child characteristics, by country
Africa
Characteristic
Benin
Ethiopia
Malawi
Households, n
1312
2697
3228
Mali
1136
Asia
Rwanda
2110
Zimbabwe
958
Cambodia
Latin America/Caribbean
Nepal
Colombia
Haiti
Peru
2049
1809
1346
1758
3662
%
Rural
Female-headed
Piped water
No sanitary facility
Electricity
Maternal
Height,1 cm
BMI,1 kg/m2
⬍18.5, %
⬎25.0, %
Education
None, %
67
13
40
71
19
90
12
13
86
7
86
19
22
20
4
71
22
27
19
12
84
17
35
3
7
67
32
43
29
34
87
18
3
85
12
93
12
34
77
18
31
21
83
12
93
67
37
51
46
32
44
13
66
30
60
158.1
21.9
11
13
157.2
20.0
26
3
155.7
21.9
6
10
161.6
21.8
9
13
158.0
22.4
6
15
159.4
22.9
5
20
152.5
20.4
20
4
150.3
20.1
24
3
154.5
24.4
3
38
158.1
22.3
10
19
150.2
24.8
1
42
71
81
32
81
33
6
32
73
3
7
38
%
Child (6–23 mo)
Stunted (HAZ ⬍ ⫺2)
Wasted (WHZ ⬍ ⫺2)
Still breast-fed
Fed complementary foods
at least the minimum
recommended number
of times (if breast-fed)2
No solid food groups in last
7 d (6–8 mo old)
28
16
88
47
18
92
47
9
93
35
18
90
40
10
91
31
9
78
36
19
81
44
18
96
16
1
47
20
8
69
22
1
75
39
43
50
25
15
42
59
68
66
22
68
28
59
5
57
19
5
22
35
8
NA3
21
1 Values are means.
2 Breast-fed children 6 – 8 mo old should be fed meals of complementary foods at least 2 times/d, with additional snacks as desired, whereas
breast-fed children 9 –23 mo old should be fed at least 3 times/d, with additional snacks (1).
3 NA, not available.
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Sample weights were used for all analyses, and statistical testing
was performed in Stata (version 7) (15). Stata allows specification of
the sample design (stratification and clustering) of the surveys.
Descriptive analyses are presented first, to provide general information on the characteristics of the study populations. They are
followed by results of the bivariate analyses of the association between children’s dietary diversity terciles and mean HAZ. The significance of differences between means was tested using an adjusted
Wald test for joint hypothesis testing. Associations were considered
significant at P-values ⬍ 0.05.
Multivariate ordinary least-squares methods were then used to test
whether associations between dietary diversity and HAZ remained
significant after controlling for several potentially confounding factors at the child (age, age squared, sex, breast-feeding status), mater-
2581
ARIMOND AND RUEL
2582
TABLE 2
Dietary diversity for children aged 6 –23 mo (food groups eaten ⱖ3 d in the last week), by country
% with low diversity
0–2 food groups
% with middle diversity
3–4 food groups
% with high diversity
5–7 food groups
3.2
2.2
2.4
1.7
2.9
3.1
38
61
57
70
42
38
31
33
37
21
43
44
33
6
6
8
16
18
2.8
2.8
44
43
40
45
15
12
4.8
(3.8)
4.5
11
(19)
13
25
(47)
29
65
(34)
58
1 The mean diversity score for Zimbabwe is on a scale of 0 – 6 because one food group was missing from the questionnaire.
2 The scores for Haiti are based on a 24-h recall, because the 7-d recall was not available.
the proportion was much lower. In general, Colombia and
Peru had more favorable household characteristics, whereas
Ethiopia consistently ranked low.
Mean maternal height was lowest in the Asian countries
and in Peru and highest in Haiti, Benin, Mali, and Zimbabwe.
The proportion of women with low BMI (⬍18.5) ranged from
1 to 11% in most countries, but was markedly higher in
Ethiopia and in both Asian countries. At the other end of the
spectrum, the highest rates of overweight and obesity (BMI
ⱖ 25) were in the 2 Latin American countries.
Maternal education and literacy varied widely among countries. In 4 countries, more than two thirds of the women
reported that they had never attended school (Benin, Ethiopia, Mali and Nepal), whereas this was reported by approximately one third of the mothers in another set of 4 countries
(Malawi, Rwanda, Cambodia, and Haiti). In the remaining
countries (Zimbabwe, Colombia, and Peru) the proportion of
women who had no schooling was ⬍10% and in these same
countries, ⬎50% of the women reported having at least some
secondary education.
Among children aged 6 –23 mo, the prevalence of stunting
(HAZ less than ⫺2 SD) was highest in Ethiopia and Malawi,
and was notably lower in all 3 countries in the LAC region.
The prevalence of wasting (WHZ less than ⫺2 SD) was
highest in Ethiopia, the West African countries (Benin and
Mali), and in both Asian countries (Cambodia and Nepal),
and very low in the 2 Latin American countries.
Feeding practices
Feeding practices for children aged 6 –23 mo also differed by
country (Table 1). Breast-feeding was maintained through y 2
of life for most children in these countries. Over 85% of the
children were still breast-fed in 5 of the 6 African countries,
and in Nepal. Rates were lowest in Colombia and Haiti. Low
frequency of feeding appeared to be a problem in most countries, and particularly in Mali, Rwanda, and Haiti. In these 3
countries, the mean frequency of feeding was ⬍2 on the day
before the survey. Late introduction of solids/semisolids was a
problem in a number of countries, and is particularly extreme
in Ethiopia and Mali, where more than half of the 6- to
8-mo-old children received none of the food groups in the
previous week.
Mean dietary diversity was lowest in Mali, followed by
Ethiopia and Malawi (Table 2). Note that in Mali and Ethiopia, the low mean reflects a large proportion of children who
received none of the food groups (Table 1); in Malawi, very
few children ate none of the groups in the previous week, yet
diversity was very low. Mean dietary diversity was observed to
be highest in Peru and Colombia.
Similar patterns were observed when examining the percentages of children with low, average, or high dietary diversity in each country, using fixed cutoff points to define these 3
categories. A very high percentage of children from Mali,
Ethiopia, and Malawi scored in the lowest diversity group
(having consumed only 0 –2 food groups on 3 or more days in
the previous week), whereas more than half of the children in
the 2 Latin American countries (Colombia and Peru) scored
in the highest diversity group (having consumed 5–7 food
groups on 3⫹ d in the previous week). Mean dietary diversity
was consistently higher in urban than in rural areas in every
country studied (not shown); this is consistent with findings
from previous analyses of other DHS data sets (16,17).
Associations between dietary diversity and height-for-age
Bivariate associations. Significant associations between
HAZ and dietary diversity terciles were found in bivariate
analyses in 9 of the 11 countries, but not in Benin or Cambodia. Differences between extreme terciles in the 9 countries
ranged from 0.26 in Haiti to 0.56 in Peru. The differences were
generally in the expected direction, but in some cases were not
consistent in direction. For example, in Malawi and Mali,
children in the middle diversity tercile had the lowest mean
HAZ.
Multivariate analyses. Associations between dietary diversity and HAZ were significant as a main effect in 7 of the
countries studied: 4 in Africa (Ethiopia, Mali, Rwanda, and
Zimbabwe), the 2 Asian countries (Cambodia and Nepal) and
Colombia (Table 3).9 In these countries, the size of the
adjusted Z-score differences between low and high diversity
9
In 2 countries (Mali and Rwanda), P-values for individual contrasts between
high and low diversity tended to be significant (P ⫽ 0.06 and 0.07, respectively),
but the joint test for significance of all contrasts was significant, P ⬍ 0.05.
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Africa
Benin
Ethiopia
Malawi
Mali
Rwanda
Zimbabwe1
Asia
Cambodia
Nepal
Latin America/Caribbean
Colombia
Haiti (24-h)2
Peru
Mean diversity score
(range 0–7)
DIETARY DIVERSITY AND CHILD NUTRITIONAL STATUS
2583
TABLE 3
Summary of regression results with dietary diversity terciles as one determinant of HAZ:
coefficients and significant main effects, by country1
Africa
Latin America/Caribbean
Benin
Ethiopia
Malawi
Mali
Rwanda
Zimbabwe
Cambodia
Nepal
Colombia
Haiti
Peru
⫺0.25*
0.00*
⫺0.10
0.05*
0.01
⫺0.24*
0.01*
⫺0.26*
0.04*
0.01
⫺0.24*
0.00*
⫺0.17*
0.04*
0.02
⫺0.19*
0.00
⫺0.10
0.03*
0.05*
⫺0.28*
0.01*
⫺0.14*
0.04*
0.02
⫺0.19*
0.00
⫺0.11
0.03*
0.03
⫺0.06
0.00
⫺0.07
0.05*
0.00
⫺0.22*
0.00*
⫺0.01
0.05*
0.04*
⫺0.11*
0.00
⫺0.18*
0.06*
0.02*
⫺0.10*
0.00
⫺0.26*
0.05*
0.05*
⫺0.16*
0.00*
⫺0.03
0.05*
0.00
⫺0.03
⫺0.03
0.18
0.37
0.01*
0.39*
0.14
0.12
0.12
0.15
0.12
⫺0.16
0.15
0.45
0.17*
0.24*
⫺0.09
⫺0.07
⫺0.09
0.04
⫺0.21
⫺0.12
⫺0.13
⫺0.03
0.10
⫺0.10
0.15
0.21
⫺0.36*
0.14
0.00
⫺0.36*
⫺0.03
0.13
⫺0.47*
0.03
0.06
⫺0.20
0.05
0.01
⫺0.60*
0.12*
0.10*
⫺0.35*
0.07
0.04
⫺0.01
⫺0.10*
⫺0.14*
⫺0.02
⫺0.07
⫺0.05
⫺0.06
0.01
0.24
26.62
1072
0.19*
0.35*
0.19
20.29
2372
0.08
0.06
0.19
33.22
2651
⫺0.11*
0.23*
0.28
14.58
849
⫺0.08*
0.17*
0.20
29.20
1802
0.11*
0.03
0.00
0.11*
0.24*
0.38
0.50
0.06
⫺0.12
0.27
0.43*
0.10
0.15
⫺0.38*
0.02
0.14
⫺0.10
0.30*
0.21*
⫺0.17
0.12
0.09
⫺0.55*
⫺0.01
0.14*
⫺0.11
0.01
⫺0.11
⫺0.28
0.14*
⫺0.09*
0.09
0.08*
0.04
⫺0.06
0.10*
0.02
⫺0.00
0.14*
0.03
⫺0.03
⫺0.15
⫺0.23*
⫺0.06
⫺0.17*
⫺0.18*
⫺0.16*
0.07*
0.23*
0.24
25.98
1632
0.06*
0.19*
0.26
23.79
1179
0.08
0.10
0.23
27.02
1574
0.03
0.09
0.29
40.98
2874
0.47*
0.68*
0.15
6.09
617
0.23*
0.37*
0.17
7.56
771
1* Significant main effect, P ⬍ 0.05. For continuous variables and dichotomous variables, each coefficient with an asterisk was significant. For
categorical variables—maternal education, number of prenatal care visits, and dietary diversity terciles— coefficients are shown to be significant if
joint tests of contrasts were significant.
2 A “⫺” indicates a negative coefficient for boys compared with girls.
3 A “⫺” indicates a negative coefficient for rural areas compared with urban areas.
4 A “⫺” indicates a negative coefficient for continued breast-feeding compared with no breast-feeding.
groups ranged from 0.24 in Colombia, to 0.59 in Zimbabwe
(Fig. 1). The bivariate associations between dietary diversity
and HAZ in Malawi, Haiti, and Peru were no longer significant as main effects in multivariate analyses that controlled for
child, maternal, and household factors. In contrast, a significant association was observed in Cambodia in the multivariate, but not in the bivariate results.
In examining two-way interactions (Table 4), dietary diversity interacted with selected characteristics in a number of
countries, including 3 of the 4 countries in which the main
effect of dietary diversity was not significant in the multivar-
iate analysis. Only Benin had no association between dietary
diversity and HAZ (no main effect and no interaction with
other factors).
The most frequently observed interactions were between
dietary diversity and the age of the child, and between diversity and current breast-feeding status (still breast-fed or not).
In 2 of the 3 countries in which dietary diversity interacted
with the child’s breast-feeding status [Cambodia (Fig. 2) and
Nepal], the findings showed that dietary diversity was more
strongly associated with HAZ among children who were no
longer breast-fed.
FIGURE 1 Adjusted mean HAZ
by diet diversity tercile in 11 countries.
Values are means ⫾ SEM, n ⫽ 617–
2874. Means were adjusted for child
age and age squared, maternal height
and BMI, number of children ⬍ 5 y old
in household, and the 2 wealth/welfare
factor scores. Differences in HAZ by
diet diversity tercile were significant as
main effects in 7 countries (Ethiopia,
Mali, Rwanda, Zimbabwe, Cambodia,
Nepal, and Colombia), P ⬍ 0.05 (joint
test of significance of categories).
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Child age
Child age squared
Gender2
Maternal height
Maternal BMI
Maternal education
Primary vs. none
Secondary vs. none
Prenatal care visits
1–3 vs. none
4⫹ vs. none
Urban or rural3
Wealth/welfare factors
1st factor
2nd factor
Still breast-fed4
Number of children
⬍5 y old
Dietary diversity tercile
Middle vs. low
High vs. low
R2
F
n
Asia
ARIMOND AND RUEL
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TABLE 4
Adjusted differences in mean HAZ between highest and lowest dietary diversity terciles, by country for subgroups
of each variable that interacted significantly with dietary diversity in multivariate analyses1
Africa
Malawi
Rwanda
0.22
⫺0.26
0.16
Cambodia
Nepal
0.67
0.08
0.42
⫺0.07
0.45
Haiti
Peru
0.34
⫺0.14
0.49
0.07
0.54
0.41
0.44
0.07
0.15
0.11
0.22
⫺0.12
0.61
0.83
0.15
⫺0.01
0.21
1.15
0.58
0.39
0.06
0.34
1.53
1 Interactions were considered to be significant when P ⬍ 0.05. There were no significant interactions in Benin, Ethiopia, Zimbabwe, or Colombia.
Positive values for differences indicate that HAZ was highest in the high diversity tercile.
The direction of the interaction between dietary diversity
and other factors was not consistent across countries. For
example, the interaction between diversity and child age
group showed that diversity was most strongly associated with
HAZ among older children in some countries (e.g., Peru),
whereas the opposite was true in Rwanda, where the strongest
association was among children 6 –11 mo old. Urban/rural
differences in the association between dietary diversity and
HAZ were also observed in 2 countries, with stronger associations in urban areas in Haiti; the opposite was true in Mali
with stronger associations in rural areas.
FIGURE 2 Interaction between breast-feeding status and dietary
diversity terciles in Cambodia DHS⫹ 2000. Values (adjusted mean
HAZ) are means ⫾ SEM, n ⫽ 771. Means were adjusted for child age
and age squared, maternal height and BMI, number of children ⬍ 5 y
old in household, and the 1st wealth/welfare factor score. The interaction was significant (P ⬍ 0.05).
DISCUSSION
This analysis of DHS data confirms that dietary diversity is
generally associated with child nutritional status, and that the
associations remain when household wealth and welfare factors are controlled for by multivariate analyses. This was
observed for a range of countries and populations with widely
different dietary patterns. Dietary diversity was significant as a
main effect in 7 countries in multivariate models, and interacted significantly with other factors (e.g., child age, breastfeeding status, urban/rural location) in 3 of the 4 remaining
countries. Thus, dietary diversity was significantly associated
with HAZ, either as a main effect or in an interaction, in all
but 1 of the countries analyzed. The existence of significant
interactions in some countries means that dietary diversity was
more strongly associated with child HAZ among some subgroups of the population.
Positive associations between dietary diversity and child
nutritional status were documented previously in China (7),
Kenya (4), Mali (8), and Haiti (18). Two additional studies in
Niger (5) and Guatemala (6) showed positive but not significant associations; however, sample sizes in both studies were
relatively small (reducing the statistical power to detect differences) and in one of these (Guatemala), the children were
younger than in the other studies (9 –11 mo). In addition to
variations in age groups, a variety of dietary methods were
used, and diversity indicators and cutoffs were defined differently in each study. The fact that a positive association between dietary diversity and child nutritional status was observed in most studies, in spite of the lack of uniformity in
methodological approaches and populations studied, suggests
that the association is robust.
Two previous studies also documented an interaction between dietary diversity and breast-feeding status. Our results
for Cambodia and Nepal confirm their findings in showing a
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Age category
6–11 mo
12–17 mo
18–23 mo
Location
Urban
Rural
1st wealth/welfare factor
Low
Middle
High
2nd wealth/welfare
factor
Low
Middle
High
Still breast-fed
Yes
No
Mali
Latin
America/Caribbean
Asia
DIETARY DIVERSITY AND CHILD NUTRITIONAL STATUS
and diversity score cutoffs) and to explore the potential to
harmonize measurement tools and indicators for universal use.
If research does establish that indicators of dietary diversity
are good and consistent predictors of nutrient adequacy, these
indicators could become invaluable tools with which to assess
dietary quality as it relates to nutrient deficiencies, and to
monitor and evaluate progress aimed at improving diet quality
for young children.
ACKNOWLEDGMENTS
The authors thank Altrena Mukuria, Casey Aboulafia, and Noah
Bartlett of ORC Macro, International for sharing information on the
DHS data sets and for discussions of preliminary results. We thank
Eunyong Chung of USAID, and Anne Swindale and Paige Harrigan
of the Food and Nutrition Technical Assistance Project (FANTA)
managed by the Academy for Educational Development for USAID
for their helpful comments on a preliminary report. We are also
grateful to Wahid Quabili of the International Food Policy Research
Institute (IFPRI) for assistance with data analysis.
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stronger association between dietary diversity and HAZ for
nonbreast-fed children (4,19). Dietary diversity may be more
important for nonbreast-fed children because they rely on
complementary food to meet all of their energy and nutrient
needs.
Other observed interactions are less consistent, and some
are difficult to interpret. There may be a variety of reasons why
diversity appears to be more strongly associated with HAZ in
subgroups. Depending on local diet patterns, high diversity
scores may be more or less nutritionally meaningful. For example, if many food groups are given, but in extremely small
quantities, diversity scores are less nutritionally meaningful. In
some subgroups, there may be a lack of nutritionally important
variation; for example, low, middle, and high terciles may all
in fact reflect quite low diversity (among the youngest children
in Mali and Ethiopia the age- and sample-specific terciles
defined high diversity as 2 or more food groups, and very few
children consumed ⬎2). There may also be 3-way interactions; this was not assessed because subgroups become too
small. Interactions do indicate that more complex relationships were present, and that coefficients for main effects were
misleading.
Some previous studies reporting associations between diet
diversity and child nutritional status did not control for likely
confounders. The purpose of controlling for wealth and welfare factors in the analysis presented here was to try to disentangle the association between dietary diversity and nutritional status from household socioeconomic status. Although
our results are a step forward in determining that the association is, at least in part, independent of socioeconomic factors,
it is important to recognize the limitations of this type of
cross-sectional analysis.
First, a child’s nutritional status as reflected in HAZ represents a long-term cumulative process, whereas the dietary
information available in the DHS reflects only the previous
week. One major and unproven assumption underlying our use
of a 1-wk recall for dietary diversity is that recent diversity is
a good proxy for longer-term dietary diversity. Note that a
failure of this assumption would likely result in a lack of
association between dietary diversity and nutritional status, a
finding obtained for only 1 country in our analysis (Benin). A
second potential limitation is in our measurement of wealth
and welfare factors. Although measurement approaches similar
to ours are increasingly popular, like other measures of socioeconomic factors, they are imperfect, and we cannot rule out
the possibility that our control for socioeconomic status was
not complete.
The motivation for focusing on simple dietary diversity
indicators, measurable in cross-sectional surveys, is to move
forward in meeting the needs of programs and research seeking
simple measurement tools. In the context of programs, or of
research with multiple objectives (e.g., large household surveys), detailed dietary assessment is usually impossible. Dietary
diversity indicators could be particularly useful in these contexts. Before they can be recommended for widespread use,
however, additional research is essential, to confirm that diversity is meaningfully associated with nutrient adequacy in
different population groups and in countries with varying
dietary patterns. In particular, relations between dietary diversity and nutrient intake, adequacy, and density, must be clarified. Additional research will also be required to address a
number of methodological issues related to the construction of
dietary diversity indicators (for example, choosing food groups
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