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Food Consumption and Nutrition Division, International Food Policy Research Institute (IFPRI), Washington, DC 20006
3To whom correspondence should be addressed. E-mail: m.arimond{at}cgiar.org.
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ABSTRACT |
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 TOP ABSTRACT SUBJECTS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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KEY WORDS: • Demographic and Health Surveys (DHS) • dietary diversity • child nutritional status • 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 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 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 breast-feeding 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.
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SUBJECTS AND METHODS |
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TOPABSTRACTSUBJECTS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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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 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 
3 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 "3 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. Country-specific 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 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
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), maternal [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.
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RESULTS |
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TOPABSTRACTSUBJECTS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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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, the proportion was much lower. In general, Colombia and Peru had more favorable household characteristics, whereas Ethiopia consistently ranked low.
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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.
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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 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.
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DISCUSSION |
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TOPABSTRACTSUBJECTS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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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 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 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.
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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2 Funded in part by the Food and Nutrition Technical Assistance Project (FANTA) managed by the Academy for Educational Development for USAID.
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 Z-score(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.
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.
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.
Manuscript received 2 June 2004. Initial review completed 1 July 2004. Revision accepted 2 August 2004.
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LITERATURE CITED |
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TOPABSTRACTSUBJECTS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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1. PAHO/WHO (2003) Guiding Principles for Complementary Feeding of the Breastfed Child 2003 PAHO/WHO Washington, DC.
2. Kant, A. K. (1996) Indexes of overall diet quality: a review. J. Am. Diet. Assoc. 96:785-791.[Medline]
3. Hatloy, A., Torheim, L. E. & Oshaug, A. (1998) (1998) Food variety—a good indicator of nutritional adequacy of the diet? A case study from an urban area in Mali, West Africa. Eur. J. Clin. Nutr. 52:891-898.[Medline]
4. Onyango, A., Koski, K. G. & Tucker, K. L. (1998) Food diversity versus breastfeeding choice in determining anthropometric status in rural Kenyan toddlers. Int. J. Epidemiol. 27:484-489.
5. Tarini, A., Bakari, S. & Delisle, H. (1999) The overall nutritional quality of the diet is reflected in the growth of Nigerian children. Santé 9:23-31.[Medline]
6. Brown, K. H., Peerson, J. M., Kimmons, J. E. & Hotz, C. (2002) Options for achieving adequate intake from home-prepared complementary foods in low income countries. Black, R.E. Michaelsen, K. F eds. Public Health Issues in Infant and Child Nutrition 2002:239-256 Nestec, Ltd., Vevey/Lippincott Williams & Wilkins Philadelphia, PA. .
7. Taren, D. & Chen, J. (1993) A positive association between extended breast-feeding and nutritional status in rural Hubei Province, People’s Republic of China. Am. J. Clin. Nutr. 58:862-867.
8. Hatloy, A., Halland, J., Diarra, M. & Oshaug, A. (2000) Food variety, socioeconomic status and nutritional status in urban and rural areas in Koutiala (Mali). Public Health Nutr. 3:57-65.[Medline]
9. Arimond, M. & Ruel, M. T. (2002) Progress in Developing an Infant and Child Feeding Index: an Example Using the Ethiopia Demographic and Health Survey 2000. Food Consumption and Nutrition Division Discussion Paper 143 2002 International Food Policy Research Institute Washington, DC.
10. Hoddinott, J. & Yohannes, Y. (2002) Dietary Diversity as a Food Security Indicator. Food Consumption and Nutrition Division Discussion Paper 136 2002 International Food Policy Research Institute Washington, DC.
11. World Health Organization (1995) Physical Status: The Use and Interpretation of Anthropometry. WHO Technical Report Series 1995 WHO Geneva, Switzerland.
12. Falkingham, J. & Namazie, C. (2002) Measuring Health and Poverty: A Review of Approaches to Identifying the Poor 2002 Health Systems Resource Centre London, UK.
13. Filmer, D. & Pritchett, L. (1998) Estimating Wealth Effects Without Expenditure Data—or Tears: An Application to Educational Enrollments in States of India. World Bank Policy Research Working Paper 1998 The World Bank Washington, DC.
14. Gwatkin, D. (2000) Health inequalities and the health of the poor: What do we know? What can we do?. Bull. WHO 78:3-18.[Medline]
15. Stata Corporation (2001) Stata Statistical Software: Release 7.0 Reference 2001 Stata Corporation College Station, TX.
16. Ruel, M. T. (2000) Urbanization in Latin America: constraints and opportunities for child feeding and care. Food Nutr. Bull. 21:12-24.
17. Ruel, M. T. & Garrett, J. (2004) Features of urban food and nutrition security and considerations for successful urban programming. e-JADE (in press).
18. Ruel, M. T., Menon, P., Arimond, M. & Frongillo, E. (2004) Food insecurity: an overwhelming constraint for child dietary diversity and growth in Haiti. FASEB J. 18:A106 (abs.).
19. Marquis, G. S., Habicht, J.-P., Lanata, C. F., Black, R. E. & Rasmussen, K. M. (1997) Breast milk or animal-product foods improve linear growth of Peruvian toddlers consuming marginal diets. Am. J. Clin. Nutr. 66:1102-1109.
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