Minimum Dietary Diversity for Women (MDD-W)

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

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

Summary

The 24-hour Dietary Recall (24HR) method provides comprehensive, quantitative information on individual diets by querying respondents about the type and quantity of all food and beverages consumed during the previous 24-hour period (Gibson & Ferguson, 2008). A standard multiple pass 24HR includes having the respondent iteratively provide increasingly granular data about each food or drink and its preparation method and other attributes, as well as an estimation of the portion size consumed. The multiple pass approach has been validated in many low- and middle-income countries (Gibson et al., 2017).

Enumerator-administered, rather than self-administered, 24HRs are often used in low- and middle-income countries because they are quick, culturally sensitive, and provide quantitative data on both foods and nutrients (Gibson et al., 2017). Data from 24HR can be used to assess dietary patterns, food groups, or nutrient intake. In order to analyze the nutrient content, the food data must be matched with nutrient information from a food composition database. Mean intakes of foods and nutrients can be measured using a single 24HR, while assessing the "usual intake" of a population requires that repeat 24HRs are collected from a sub-sample of the study population (Gibson & Ferguson, 2008).

Individual-level quantitative dietary data can also be used to develop a better understanding of typical household food preparation, cooking methods, and brand names of foods consumed within the household. Furthermore, if individual-level dietary data are collected in conjunction with information on socioeconomic status, education, and health, the data can be used to examine linkages between income levels and dietary choices, as well as dietary patterns and health outcomes.

While 24HRs offer a higher degree of accuracy in assessing food and nutrient intake relative to Food Frequency Questionnaires (FFQ) or estimates derived from Household Consumption and Expenditure Surveys (HCES), they are collected infrequently on nationally representative samples, and when they are collected, these data are typically not publicly available (Pisa et al., 2018). However, there is increasing interest in, and demand for, individual-level quantitative dietary data, particularly in light of the nutrition transition and rapid food system changes (Coates et al., 2017). For example, the Food and Agriculture Organization and World Health Organization Global Individual Food consumption data Tool (FAO/WHO GIFT) aims to make publicly available existing quantitative individual food consumption data from countries all over the world. In addition, the Global Dietary Database (GDD) provides information on dietary intakes of foods and nutrients for children and adults by age, sex, pregnancy/nursing status, rural/urban, and level of education.

Strengths:

• 24HRs provide quantitative estimates of individual food consumption and nutrient intake
• Can structure survey—and analyses—to include information on food sources and preparation methods
• Can account for foods consumed together that may enhance or inhibit micronutrient absorption
• Offer a higher degree of accuracy in assessing food and nutrient intake relative to FFQs or estimates derived from HCES

Weaknesses:

• Given relative complexity of 24HR surveys, significant training of enumerators is required to minimize errors in data collection
• Accurately recalling the quantity consumed can be challenging for respondents and a relatively large source of error in 24HRs compared to enumerator-administered Weighed Food Records (WFR)
• Data are frequently collected from small samples that are not nationally representative
• As with other surveys that rely on memory and are administered by an enumerator, there is potential for recall bias and interviewer bias
• Like most surveys, to capture seasonal variation data collection must span the entire year or be repeated in multiple seasons

Summary

Weighed Food Records (WFR), also called weighed food diaries or simply weighed records, are considered the "gold standard" of individual quantitative dietary assessment methods (Carlsen et al., 2010). WFR require the respondent or enumerator to weigh all foods and beverages at the time of consumption (rather than asking respondents to recall their consumption, as is done in the 24-hour quantitative recall, or 24HR). Any plate waste must also be recorded, as well as a description of the food along with preparation methods and brand names.

Though no dietary assessment methodology can completely prevent measurement error, WFR are often considered the most precise method when it comes to quantifying food intake, since each food is weighed, eliminating issues associated with portion size estimation through recall. As a result, the high degree of accuracy produced by WFR means they are often used as the reference method in validation studies of other dietary assessment methods (e.g. Alemayehu et al., 2011; Nightingale et al., 2016).

If working in a low- or middle-income country with low literacy levels the presence of a trained enumerator in the household is typically required throughout the period being assessed, from the time the first food or beverage is consumed in the morning to when the last one is consumed at night. When using WFR to collect dietary data, enumerators must be carefully trained to standardize measurement, instrument calibration, and interviewing methods in order to reduce measurement error. While enumerator-administered WFR provide very accurate estimates of dietary intake, they can be intrusive and time-consuming, as well as potentially distort the behavior of respondents’ due to the presence of an enumerator throughout the day (Ortega et al., 2015).

Furthermore, administering a WFR can be difficult in populations where people are out of the home for all or most of the day (e.g. urban areas or school age children), as the enumerator would have to accompany respondents throughout the day. If the population of interest is literate, then a food diary or self-administered WFR could be used, in which the respondent weighs and records all of the foods and beverages consumed over a specified period (e.g. 24 hours). Due to the expense and small sample size of most WFR collections, care must be taken to ensure that the sample is representative of the studied population (Wrieden et al., 2003).

Strengths:

• Offer a high degree of accuracy in assessing food and nutrient intake relative
 to other recall-based dietary assessment methods
• Provide quantitative estimates of individual food consumption and nutrient intake
• Take into account preparation methods and the effect on estimated nutrient content
• Applicable to diverse groups with a wide range of dietary patterns

Weaknesses:

• Significant training is required to minimize errors in data collection
• Data are frequently collected from small samples that are not nationally representative
• Enumerator-administered WFR are intrusive and can distort respondent behavior
• Like most surveys, to capture seasonal variation data collection must span the entire year or be repeated in multiple seasons