Minimum Dietary Diversity (MDD-IYCF) for children 6-23 months old

Summary

Variation in an individual's diet is associated with the intake of adequate energy and essential nutrients; increasing variety in one’s diet is recommended in most dietary guidelines globally (Ruel, 2003). Dietary diversity is especially important among populations with diets based on starchy staples where micronutrient deficiency is more likely (Ruel, 2003). The most common method of measuring dietary diversity for a household or individual consists of assessing the variety of different food groups consumed in a specific recall period; information on the quantity of foods consumed is not gathered. Indicators of dietary diversity are considered to be useful as measures of impact for programs designed to address nutrition through agricultural pathways. 

Dietary diversity can be measured at either the household or the individual level and higher scores represent a more diverse diet. For households, a higher score is an indicator of increased economic access to a varied diet for household members (though the indicator does not reflect intra-household dietary patterns). Household dietary diversity has been shown to be associated with caloric and protein adequacy and household income (Swindale & Bilinksy, 2006).

Individual dietary diversity indices, specifically the Minimum Dietary Diversity for Women (MDD-W) and the Minimum Dietary Diversity (MDD) for children 6-23 months, have been shown to be a rough proxy for diet quality and nutrient adequacy (FAO & FHI, 2016; Moursi et al., 2008). While there is consensus around the significance of dietary diversity, there are multiple approaches to measurement with varied food groups and recall periods (Table 1).

Table 1

Dietary diversity indicators can be constructed using a specific module relevant to that dietary diversity indicator (e.g. Household Dietary Diversity module, MDD module for children from 6-23 months). In addition, the various dietary diversity scores can be constructed from existing data, as long as the recall period is aligned. Some potential data sources include Household Consumption and Expenditure Surveys (HCES), Demographic and Health Surveys (DHS) & Multiple Indicator Cluster Surveys (MICS), or Food Frequency Questionnaires. More generally, dietary diversity modules are frequently included as short modules in multi-purpose household survey questionnaires.   

Dietary diversity scores are not direct measures of consumption and not all have been validated as proxy measures of nutrient adequacy. A significant drawback of the household-level indicators is that scores do not provide information on whether the household dietary diversity is shared equally by all individual members of the household. For more precise population measures of nutrient adequacy by age/sex groups individual-level data from 24-hour Dietary Recalls or Weighed Food Records should be used.

Strengths

• Relatively easy to use and to integrate as a short module into surveys
• Requires fewer resources than attempting to measure quantitative dietary consumption data for nutrient adequacy
• Calculating the scores is a straightforward process and training others to collect data does not require a large amount of time
• Dietary diversity scores can give an idea of what types of foods are consumed

Weaknesses

• Modules usually require tailoring to specific contexts
• Scores do not provide detailed information on quantitative dietary intakes and are not a direct measure of nutrient adequacy; the cut-offs for the MDD-W do not predict nutrient adequacy in all contexts for all population groups
• Household-level dietary diversity scores do not provide information on individual household members and cannot be used to draw conclusions about individuals

Summary

The Demographic and Health Survey (DHS) Program has been supported by USAID for over 30 years (DHS, 2018). More than 320 surveys in over 90 countries have been conducted since the program’s inception, gathering information on select nutrition indicators, as well as fertility, reproductive health, maternal health, child health, immunization, HIV and AIDS, maternal and child mortality, malaria, and other indicators (Fabic et al., 2012). DHS data can be explored online through the DHS STATcompiler or downloaded for further analysis (DHS, 2018).

The Multiple Indicator Cluster Surveys (MICS) program is the largest household survey program on children and women worldwide. The MICS was developed by UNICEF to assist countries in filling data gaps on children’s and women’s health statuses (UNICEF, 2018). MICS was officially launched in 1994 in South Asia with 28 indicators, and now includes over 300 surveys in 112 countries, with 237 distinct indicators (counting those requiring sex disaggregation). The MICS data on infant and young child feeding can be explored online through an interactive portal, as well as downloaded for further analysis (UNICEF, 2018).

Neither DHS nor MICS includes comprehensive food consumption data, although the standard questionnaire includes several infant and young child consumption indicators on breastfeeding and feeding practices. The data collected in both surveys represent only children age five years old and under and women ages 15-49 years old. In addition, useful analyses can be carried out to understand the relationship between infant and young child feeding practices and nutritional outcomes (e.g. stunting, wasting). Several specific food consumption indicators can be calculated with DHS and MICS data (USAID, 2017; UNICEF, 2018), including:

• Initial breastfeeding
• Breastfeeding status
• Median duration and frequency of breastfeeding
• Percentage of children 6-23 months who are fed according to infant and young child feeding practices

DHS and MICS are designed to be nationally representative, with typical sample sizes ranging from 5,000 to 30,000 households. These types of surveys are usually repeated in a given country every three to five years.

Strengths:

• Publicly available, well-documented data sources that are free to use
• Provide information on trends over time as data are collected routinely in many countries (~5 years)
• A core set of survey modules are standardized across countries allowing for comparability over time and place; some countries include additional modules (e.g. biomarker data)

Weaknesses:

• Do not include food consumption data on entire diet
• Only representative of children under five years old and women 15-49 years old
• Relies on proxy-reporting by one adult female for children under five

Summary

Food Frequency Questionnaires (FFQ) are a type of dietary assessment instrument that attempts to capture an individual’s usual food consumption by querying the frequency at which the respondent consumed food items based on a predefined food list. Given that food lists are culturally specific, FFQs need to be adapted and validated for use in different contexts (Thompson & Subar, 2013).

FFQs are a common method for measuring dietary patterns in large epidemiological studies of diet and health. FFQs are often limited to the food items that are a source of nutrients related to the particular dietary exposures under study, for example, fruit and vegetable consumption or foods with high levels of saturated fat. Dietary diversity scores are a type of metric that are often calculated from a simplified FFQ (see the description of Dietary Diversity metrics to learn more). Food consumption modules of Household Consumption and Expenditure Surveys (HCES) that use a food list and an extended recall period can also be considered a type of FFQ.

In general, FFQs rely on a longer recall period in order to capture foods that are not consumed every day but are still part of the individual’s typical diet. FFQ recall periods vary greatly, but typically range from 7 to 30 days (although some are as long as one year). A drawback is that recall bias may increase with longer periods of recall (Coates et al., 2012). However, these measures of ‘usual intake’ are a more valid indicator of the relationship between diet and health outcomes than those capturing only a single 24-hour snapshot of the diet (24-hour Dietary Recalls can only provide information on usual intake if data are collected from respondents on multiple non-consecutive days). Longer FFQs can better assess total diets, but shorter FFQs have higher response rates and lower respondent burdens (Thompson & Subar, 2013).

FFQs typically collect information on the frequency of consumption but not necessarily on the quantity consumed. When FFQs do include questions about quantity consumed it is typically based on standard portion sizes, rather than direct weight or use of household utensils. Therefore, FFQs are not as accurate as other quantitative dietary assessment methods (e.g. 24-hour Dietary Recall) (Coates et al., 2012). Additional measurement error is introduced when food lists are not specific to the studied population, when questionnaires use inconsistent or imprecise portion sizes (Shim et al., 2014), or when the food lists are not granular enough to make an accurate match to a food composition table for deriving nutrient content of the diet. Because food lists are developed with a specific population in mind, it can be difficult to accurately compare results across populations (cultures or countries) with different dietary patterns.

Strengths:

• Better at estimating ‘usual diet’ due to longer recall period than the 24-hour Dietary Recall or 24-hour Weighed Food Records
• Captures individual-level dietary patterns
• FFQs can be easier and less time-consuming to implement than a 24-hour Dietary Recall, if the food list is relatively short (e.g. <100 items)

Weaknesses:

• FFQs require substantial up-front investment to develop and validate the instrument (food list and quantities) for a given context or country.
• Usual frequency of intake is prone to measurement error, particularly with recall periods longer than seven days (and usual portion size questions are prone to measurement error)
• If the FFQ is too long it can be more time consuming to administer than a standard 24-hour Dietary Recall and cause respondent fatigue
• Like most surveys, to capture seasonal variation data collection must span the entire year or be repeated in multiple seasons