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Journal of Nutrition, doi:10.3945/jn.108.093971
Vol. 138, No. 12, 2448-2453, December 2008
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© 2008 American Society for Nutrition

Community and International Nutrition

Dietary Diversity Is a Good Predictor of the Micronutrient Density of the Diet of 6- to 23-Month-Old Children in Madagascar1–3,

Mourad M. Moursi4,5,*, Mary Arimond6, Kathryn G. Dewey7, Serge Trèche4, Marie T. Ruel6 and Francis Delpeuch4

4 UR106 NALIS, Institut de Recherche pour le Développement, 34394 Montpellier, France; 5 Doctoral School 393, Université Pierre et Marie Curie, 75005 Paris, France; 6 Food Consumption and Nutrition Division, International Food Policy Research Institute, Washington, DC 20006; and 7 Program in International Nutrition and Community Nutrition, University of California, Davis, CA 95616

* To whom correspondence should be addressed. E-mail: moursi{at}mpl.ird.fr.


   ABSTRACT
TOP
 ABSTRACT
Introduction
 Materials and Methods
 Results
 Discussion
 LITERATURE CITED
 
This study was conducted in the context of a multicountry validation of indicators of diet quality and had the following objectives: 1) to determine how well dietary diversity scores (DDS) predict diet quality of children aged 6–23 mo in urban Madagascar; and 2) to assess whether the prediction was improved by changing the food groups included and by imposing a minimum amount restriction. Correlation and regression were used to describe the relationship between 4 diversity scores (2 based on 8 and 7 food groups, the latter excluding fats and oils, and 2 that imposed a 10-g minimum restriction on food groups) and the mean micronutrient density adequacy (MMDA) of the diet. MMDA, the dietary quality score used, was calculated as the mean individual micronutrient density adequacy for 9 or 10 "problem" nutrients (depending on age and breast-feeding status), each capped at 100%. We used sensitivity and specificity analysis to determine how well DDS predicted MMDA below or above selected cut-offs. All scores were positively correlated with MMDA. When the fats and oils group was omitted, correlations were 10–16% higher for breast-fed children and 19–28% higher for non-breast–fed children. Correlations were only slightly improved with the 10-g minimum. With the 7-food group score, a score of ≤2 best predicted low dietary quality (MMDA <50%), with 64% sensitivity, 82% specificity, and 22% misclassification. Imposing a 10-g minimum increased misclassification (30%). These results support the growing evidence of the usefulness of dietary diversity to predict dietary quality, and among infants and young children more specifically.


   Introduction
TOP
 ABSTRACT
 Introduction
Materials and Methods
 Results
 Discussion
 LITERATURE CITED
 
The Global Consultation on Complementary Feeding convened by the WHO in 2001 identified lack of indicators as one of the factors constraining progress in improving young child feeding (1,2). In response, researchers have engaged in the process of developing and validating simple indicators of appropriate feeding for population-level use in developing countries. Dietary diversity, which is defined here as the number of different food groups consumed over a given reference period, has been identified as a potentially useful indicator.

Dietary diversity has been shown to be associated with increased nutrient adequacy of children and adults in developed countries (3,4). Similar results showing that higher dietary diversity was associated with increased nutrient intake or better child nutritional status were also found in developing countries (5–10). A review of studies conducted in developing countries (11) concluded that further research was needed to refine dietary diversity scores (DDS)8 and to assess their utility to accurately reflect diet quality in young children.

Recent validation studies have provided new evidence on the relationship between DDS and micronutrient adequacy or intake of 1- to 8-y-old non-breast–fed (non-BF) children (12–14). These studies showed that DDS were positively correlated with micronutrient intake (13,14) and density (12). A DDS score of 4–5 (out of a maximum of 9–10) was found to be the best compromise between sensitivity and specificity for detecting low micronutrient adequacy, but there was no clear preferred score for high adequacy. Furthermore, in the Philippines (12), the authors assessed the effect of imposing a 10-g minimum food group requirement for the calculation of DDS and found that it improved the score's correlation with measures of micronutrient adequacy and the score's ability to predict low adequacy with reduced misclassification.

Although these studies have helped determine the utility of DDS for non-BF children, the utility of DDS for breast-fed (BF) and non-BF infants during their first year of life remains unknown. Moreover, to our knowledge, the key question of the optimal selection of food groups for the calculation of the DDS has not yet been explored.

This study addresses these questions by comparing how diversity scores (based on different food groups with or without portion size restrictions) relate to micronutrient density of the diets of 6- to 23-mo-old children in urban Madagascar.


   Materials and Methods
TOP
 ABSTRACT
 Introduction
 Materials and Methods
Results
 Discussion
 LITERATURE CITED
 
This work was part of an international effort led by the International Food Policy Research Institute, the WHO, the University of California at Davis, and the Food and Nutrition Technical Assistance Project. This study followed a protocol developed by the working group on infant and young child feeding indicators (15), which guided variable construction and analysis.

    Sample selection and study design. The study was conducted within the Nutrimad project (16). The study population was composed of mother-infant dyads in the districts of Sahalava and Antsororokavo, Fianarantsoa, Madagascar. On the basis of information on population size, sociodemographic factors, and prevalence of child malnutrition, these 2 districts had characteristics similar to other poor urban districts of Fianarantsoa.

Children aged 0–18 mo who met the following eligibility criteria were invited to participate: born in the districts; absence of illness requiring hospitalization and of malformation affecting anthropometric measurements; no intention of moving away in the next 6 mo. A total of 702 eligible infants were included, 14 mothers refused to participate, and 51 children subsequently dropped out. The study was conducted for 7 mo (April–October 2004) and comprised 3 visits at 3-mo intervals (±15 d).

This analysis was conducted on children aged 6 mo or more at any visit and for the purpose of this analysis, the same child at different visits counted as separate observations (i.e. the unit of analysis was the child x visit). The total number of available child-days was 1667.

    Data collection and analysis. Details of anthropometric measurements were previously described (17). Dietary intake of infants was estimated using a quantitative maternal 24-h recall. Portion sizes and quantities of ingredients used to prepare mixed recipes were determined using standardized household measures (18). Visual aids were available to assist respondents in accurately reporting portion sizes.

A food composition table was compiled using values from the FAO's World Food Dietary Assessment System (version 2; University of California) and the USDA nutrient database release 18 (19).

    Construction of DDS. Four different DDS were used. Two DDS summed a total of 8 possible food groups (DDS8 and DDS8-R). The 8 food groups were: grains, roots, and tubers; legumes and nuts; dairy products; flesh foods (meat, fish, poultry, and liver/organ meats); eggs; vitamin A-rich fruits and vegetables (>130 retinol equivalents/100 g); other fruits and vegetables; and fats and oils. A score of 1 was assigned if a child ate 1 or more foods from a given food group and 0 if not. These values were then summed up for all food groups with a range of 0–8 for DDS8 and DDS8-R. For DDS8, a food group scored 1 if at least 1 g was consumed. For DDS8-R, a food group was counted only if at least 10 g were consumed, except for fats and oils, for which the cut-off of ≥1 g was used. Two additional scores were calculated after excluding the fats and oils group (DDS7 and DD7-R) using the 1-g and 10-g minimum cut-offs (range 0–7).

    Mean micronutrient density adequacy. The quality of the diet was defined on the basis of micronutrient density (amount per 100 kcal or 418 kJ of food) because of the variability in breast milk intake among children. Average breast milk intake was assumed for BF children (20) and recommended nutrient intakes (RNI) were taken from FAO/WHO recommendations (21), except for calcium (22) and zinc (23).

For BF infants aged 6–11 mo, 9 key micronutrients were considered (vitamin A, thiamin, riboflavin, vitamin B-6, folate, vitamin C, calcium, iron, and zinc) (20). For BF children aged 12–23 mo and for all non-BF children, vitamin B-12 was also included, for a total of 10 key micronutrients. To derive the desired nutrient density values, the estimated amount contributed by breast milk (20) was subtracted from the RNI for each micronutrient. The individual micronutrient density adequacy was calculated as the percentage of the desired nutrient density specific for age and breast-feeding group (15). The mean micronutrient density adequacy (MMDA) was calculated as the mean of all micronutrient density adequacies, with each capped at 100%. Dichotomous variables were constructed to identify child-days with low MMDA (<50%) and better MMDA (≥75%).

    Statistical analysis. Correlation and regression were used to assess the strength of the relationship between DDS and MMDA. Interaction terms testing consumption of infant formula or fortified foods were not significant at P ≤ 0.10. We tested for linear trends by adding a quadratic term to the regression equation (DDS2); a significant coefficient for this term was considered as an indication of deviation from linearity. We used cross-sectional time-series regression models to adjust the correlation coefficients of the relationship between dietary diversity and MMDA for multiple observations from the same child and results were unchanged.

We calculated the area under the curve (AUC) for the receiver operating characteristic relationship and tested whether it differed significantly from the null value of 0.5; a null value would indicate that a given DDS has no predictive value. The performance of the different diversity scores was compared by testing differences in AUC, with greater AUC indicating better performance. Sensitivity and specificity analysis (24) was conducted to compare the utility of diversity scores for correctly identifying cases with low or better MMDA. Statistical analyses were conducted using Stata 8.2 (StataCorp); P-values < 0.05 were considered significant.

    Ethics. The study protocol was approved by the health department of Fianarantsoa province. Verbal informed consent to participate was obtained from all subjects.


   Results
TOP
 ABSTRACT
 Introduction
 Materials and Methods
 Results
Discussion
 LITERATURE CITED
 
Seventy-eight child-days (4.7%) were excluded from this analysis because of illness with loss of appetite on the day of dietary collection. Their mean length-for-age did not differ (P = 0.20) from that of the remaining children.

Breast-feeding is common in Madagascar and most children (79%) were still breast-fed during their 2nd y of life (Table 1). The prevalence of stunted children was high across all age groups. By contrast, wasting was low at all ages. Mean caretaker age was 28 y and 42% had a low level of schooling. The sample was almost equally composed of boys (51%) and girls (49%).


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  		TABLE 1 Sample characteristics1

 
Mean dietary diversity was higher for non-BF infants than for BF infants (Table 2). This was not due to age confounding (80% of non-BF infants in our samples were aged 12–23 mo), because it remained significant after adjustment for age (P = 0.04). As expected, mean DDS decreased when the 10-g minimum restriction was applied.


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  		TABLE 2 Sample distribution of dietary diversity, nutrient intakes from complementary foods (CF), and MMDAby age and breast-feeding status1

 
In general, micronutrient intakes were below the recommended values except for vitamin C for BF children and folate for BF children 6–11 mo of age. At 6–8 mo of age, the percentage of BF infants with MMDA <50% was high but decreased with age. The percentage of those with MMDA ≥75% was low among BF infants 6–8 mo of age but was higher for older children and for those non-BF.

Patterns of food consumption varied at different DDS8 scores (Table 3). Grains, roots, and tubers were the main food source at a DDS of 1. Other fruits and vegetables were consumed by more than one-half of the children at a DDS of 2 and >90% at a score of 4. Fats and oils were commonly consumed starting at diversity scores of 3 and higher. Flesh foods were consumed by at least one-half of the children at DDS of 4 and higher, whereas for legumes and nuts, dairy products, vitamin A-rich fruits and vegetables, and eggs, this only occurred at DDS of 6 and higher. Patterns of food group consumption were similar for DDS8-R, DDS7, and DDS7-R.


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  		TABLE 3 Percent of child-days on which different food groups were consumed by DDS for children aged 6–23 mo (8 food groups, 1-g minimum)1

 
All 4 DDS were strongly and positively associated with MMDA for all age groups and irrespective of breast-feeding status (Table 4). Relative to DDS8, correlations between DDS7 and MMDA were 10–16% higher for BF children and 19–28% higher for non-BF children. The same pattern was observed for DDS7-R and DDS8-R. The relationship between both DDS7 and DDS7-R and MMDA was linear at all ages. Imposing a 10-g minimum also slightly improved the coefficients and more so for the non-BF infants.


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  		TABLE 4 Correlations of DDS with MMDA by age and breast-feeding status1

 
It was not possible to conduct either the receiver operating characteristic or the sensitivity/specificity analyses for non-BF children at the MMDA <50% cut-off because of small sample size [18 child-days (8%) were below the cut-off]. All AUC values differed significantly from the null value of 0.5. (Supplemental Table 1). Nearly all of the AUC values for MMDA <50% were >0.75. Most AUC values for DDS7 were significantly greater than those for DDS8, meaning that DDS7 performed better than DDS8. When these analyses were repeated using the 10-g minimum (DDS7-R), only 1 AUC value was significantly higher than for DDS7.

For BF children 6–23 mo of age, using DDS7 for predicting low MMDA, a cut-off point of ≤2 food groups produced the best trade-off between sensitivity (64%) and specificity (82%) while keeping misclassification at 22% (Supplemental Table 2). The same cut-off also offered the best compromise for DDS7-R; relative to DDS7, sensitivity (82%) was improved at the expense of specificity (67%), but misclassification was higher (30%).

There was no clear cut-off for predicting better MMDA. No acceptable trade-off between sensitivity and specificity could be found using DDS7-R. The trade-off between sensitivity and specificity using DDS7 occurred at the cut-off of ≥4, but misclassification was high (31%).


   Discussion
TOP
 ABSTRACT
 Introduction
 Materials and Methods
 Results
 Discussion
LITERATURE CITED
 
Diversity scores seem to be useful proxies of diet quality. All DDS were positively and strongly correlated with MMDA in a consistent manner for all age groups and for both BF and non-BF children. Correlation coefficients ranged from 0.47 to 0.62 for BF children and 0.36 to 0.54 for non-BF children. Imposing a 10-g minimum requirement on food groups only slightly improved the correlations with MMDA.

The sensitivity/specificity analysis showed that, for BF children, DDS predicted low micronutrient adequacy (MMDA <50%) with reasonable sensitivity and specificity. A cut-off of ≤2 food groups resulted in moderately low misclassification, especially with the 7-group DDS (without a 10-g restriction), while achieving reasonable balance between sensitivity and specificity. No clear cut-off using MMDA ≥75% was found.

    Comparison with other studies. Correlation coefficients for the association between DDS and diet quality in non-BF infants in our study differed from those of other validation studies (12–14). This is explained by several methodological differences, the first of which was the use of other measures of diet quality such as mean adequacy ratio (14) and mean probability of adequacy (12,13). Calculation of mean probability of adequacy is based on the estimated average requirements and the SD of the requirement distributions, not the RNI; estimated average requirements/SD are not available for most micronutrients for infants <12 mo old, so we did not take this approach. Also, because we did not have total breast milk intake data (and thus total nutrient intake), we used micronutrient densities (per 100 kcal, 4184 kJ) to characterize diet quality. Despite differences in magnitude of coefficients, all studies, including ours, found a positive correlation of DDS with measures of diet quality and/or quantity, thus confirming the usefulness of DDS to reflect diet quality.

    Choice of food groups to include in DDS. Earlier DDS included fats and oils (12–14,25,26) and we therefore included this group in 2 scores. However, we also explored whether the indicator could be improved by excluding this group, because, with the exception of red palm oil, fats and oils generally do not contribute substantially to intake of the "problem" micronutrients. DDS7 and DDS7-R performed more favorably than DDS8 and DDS8-R in all analyses, achieving stronger correlations, higher AUC, and better balance between sensitivity and specificity. There are arguments against excluding fats and oils from diet quality indicators, particularly in light of recent findings demonstrating the role played by essential fatty acids in infant growth (27). These findings might point to the necessity to incorporate essential fatty acids into measures of diet quality and to accord them the same level of attention as to key micronutrients. Alternatively, fats and oil could be measured as a separate indicator of diet quality and kept independent from the DDS.

    Imposing a 10-g minimum or addressing portion size in a DDS? For non-BF infants, imposing a 10-g minimum quantity increased correlation coefficients, especially among non-BF children. However, this did not translate into consistent improvements in classifying children into those with low and those with better MMDA. Although sensitivity increased with the use of the 10-g minimum, this has to be balanced against the impracticality and additional difficulties that this poses in the field. Also, increased sensitivity was accompanied by higher misclassification and reduced specificity (i.e. more false positives). If the indicator is used for targeting, false positives result in higher costs. Therefore, the usefulness and feasibility of these refinements will ultimately depend on time and resources available and the final use that will be made of the indicator.

    Using a DDS for assessing "low" and "better" diet quality. This study contributed to a larger, 10-site study aimed at identifying population-level indicators for assessing young child feeding (28) and potentially for geographic targeting of programs. For these purposes, it is reasonable to aim for a balance between sensitivity and specificity, while also considering overall misclassification. Results from 1 site should not be used to make decisions about universal indicators. However, our results can serve to illustrate the trade-offs and results that are relevant for this site.

Our findings indicate that DDS are better at identifying children with low micronutrient adequacy rather than those with high adequacy. This is partly due to the fact that the requirements for certain micronutrients such as iron are so high (particularly for infants <1 y) that no children in our sample actually achieved 100% MMDA with their usual diet and only 30% achieved an MMDA ≥75%.

    Limitations. One limitation of this study is the use of 1 24-h recall to assess micronutrient density adequacy as well as uncertainties about quantification of amounts of foods consumed and nutrient composition data. Though precautions were taken during the collection and analysis of data, there are inevitably some errors, which are inherent to this type of research. Another limitation is the use of 24-h recall data to calculate DDS as opposed to questions specifically designed for field use. However, one objective of the exercise was to develop the simplest possible meaningful DDS to minimize potential difficulties with recall. Although field testing is needed, recall may be facilitated by the broad food groups and lack of need for information on quantities. A mother or caregiver would only need to report (yes/no) whether her infant ate any food based on grains, etc. In addition, dietary diversity has been consistently associated with dietary quality (often measured as nutrient adequacy) despite differing methods and scoring systems (11), confirming that the association is robust.

In sum, this study showed that DDS are useful proxies of micronutrient density of foods consumed by BF and non-BF infants. The performance of DDS could be improved by omitting the fats and oils food group, yielding stronger correlations with micronutrient density and better balance between sensitivity and specificity. Applying a 10-g minimum did not substantially improve the performance of DDS in identifying children with low or high micronutrient adequacy and thus might not be warranted, given that it adds considerable complexity to data collection. These results add to the growing useful evidence base for the selection of indicators, field testing, and further dialog toward consensus.


   FOOTNOTES
 
1 Supported by the French Ministry of Foreign Affairs and the European Union. Conducted within the Nutrimad project jointly led by the Groupe de Recherche et d'Échanges Technologiques, the biochemistry department of the University of Antananarivo (LABASAN), and the Institut de Recherche pour le Développement. Back

2 Author disclosures: M. Moursi, M. Arimond, K. Dewey, S. Trèche, M. Ruel, and F. Delpeuch, no conflicts of interest. Back

3 Supplemental Tables 1 and 2 and supporting material are available with the online posting of this paper at jn.nutrition.org. Back

8 Abbreviations used: AUC, area under the curve; BF, breast-fed; DDS, dietary diversity score; DDS7 and DDS8, dietary diversity scores based on 7 and 8 food groups, respectively; DDS7-R and DDS8-R, dietary diversity scores with 10g minimum consumption restriction; MMDA, mean micronutrient density adequacy; non-BF, non-breast–fed; RNI, recommended nutrient intake. Back

Manuscript received 4 June 2008. Initial review completed 17 July 2008. Revision accepted 9 September 2008.


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 ABSTRACT
 Introduction
 Materials and Methods
 Results
 Discussion
 LITERATURE CITED
 

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