Diet Quality Questionnaire (DQQ)

Overview

The Diet Quality Questionnaire (DQQ) is a standardized tool adapted to each country that assesses diet quality by identifying the most frequently consumed foods, called sentinel foods, within each food group at the population level. It can also be applied at subnational levels, such as regions, districts or urban areas, provided the food lists reflect the local setting. It has been used in national surveys like the DHS and Gallup.

The DQQ generates indicators of dietary adequacy such as the minimum diet diversity for women (MDD-W), as well as indicators for NCD risk such as the global dietary recommendation score (GDR). The DQQ has an accompanying, country-adapted tool for use with infants and young children called the IYCF-DQQ. The IYCF-DQQ measures healthy and unhealthy eating practices in this target group, and food groups are aligned with the adult DQQ, which can facilitate data collection, analysis and interpretation of results, particularly for diet quality monitoring systems and/or surveys with paired mother – child questionnaires such as the Demographic and Health surveys (DHS).

See DQQ results from 120 countries

Data from the DQQ also feeds into the Food Systems Dashboard

Rationale

Aligns with commonly used dietary quality indicators, easy to use and incorporate into existing surveys, automated analysis, low cost with no need for a food composition table. The DQQ is adapted to 120 countries, with translations into national languages and commonly used foods unique to each country. Data has been collected in many countries as a part of the Gallup poll as well as part of the DHS and Feed the Future programs. Data collection for the DQQ is quick, taking only five minutes to administer and provides comparable food group level consumption data and a straightforward way to generate MDD-W and other diet quality indicators. consumption data and a simple way to generate MDD-W and other diet quality indicators.

Type of data

The DQQ uses a binary (yes/no) questionnaire for recall over a  24-hour reference period to assess consumption across 29 predefined food groups. Of those, 18 are considered health-protective (e.g., vegetables, fruits, legumes, whole grains), and 11 are considered unhealthy or risk-associated (e.g., sugar-sweetened drinks, ultra-processed snacks, fast food). The DQQ does not collect quantitative intake data (i.e., grams or portion sizes). Each food group is assessed using sentinel foods, and if any amount of that food is eaten, it is marked “consumed.” These are aggregated to generate indicators like the Global Dietary Recommendations (GDR) score (see Indicator section below) and its sub-metrics, which reflect adherence to World Health Organization (WHO) guidelines. The tool is designed for population-level or sub-group level analysis, making it suitable for tracking diet quality trends, comparing across regions or countries, and informing the design, monitoring and evaluation of nutrition programs and policies.

Measured at the population or subgroup level, it can be used for comparisons within or across countries, to track population-level changes in diet quality, as for the design, monitoring and evaluation of programs and policies to improve diet quality.

Indicators

GDR score

The Global Diet Recommendation (GDR) score is a measure of dietary quality and diversity using food-group consumption data aligned to the World Health Organization (WHO) guidelines for healthy diets. The GDR score ranges from 0-18, with higher scores indicating more recommendations met. Recall data is collected from food consumption in the past day and night. Like other simpler dietary assessment tools, it cannot be used to measure nutrient adequacy because it does not measure quantities consumed.

The GDR score – like the GDQS – is designed to have two sub-components. The GDR and its sub-metrics (see below) are recommended as they are designed to assess the extent to which the diet is protective against diet-related NCDs and for diet-related NCD risks.

*Can also be tabulated using the GDQS, 24HR recall (quantitative)

GDR Score-
sub-metrics

The NCD-Protect and NCD-Risk scores are interpretable and context-sensitive tools for tracking diet-related NCD risk. Healthy sub-metrics are robust for tracking nutrient adequacy and diet quality improvements. Unhealthy sub-metrics require further refinement and validation to reliably capture moderation and NCD risk. NCD-Risk showed moderate correlations and lower agreement, reflecting greater variability in how unhealthy food intake is captured across contexts (Hanley-Cook 2024).

NCD-Protect: An average score for the population based on food consumption from nine health-protective food groups. Higher scores show the presence of more healthy foods and correlates positively with achieving global dietary recommendations (e.g., daily 400 g of fruits and vegetables, whole grains, pulses, nuts, or seeds, and at least 25 g fiber) (Herforth 2020).

NCD-Risk: a population-level indicator that reflects the consumption of foods associated with increased risk of noncommunicable diseases (NCDs). It is based on whether individuals consumed any of eight food groups to limit or avoid, such as sugar-sweetened beverages, processed meats, and fried or fast foods, during the previous day. This indicator is also a proxy for ultra-processed food intake. A higher NCD-Risk score indicates greater intake of unhealthy foods and is negatively associated with meeting WHO global dietary recommendations (i.e., <10% total energy from free sugars, <10% of total energy from saturated fat, <30% from total fat, <5g of salt daily, <350–500g red meat weekly) (Herforth 2020). The NCD sub-metrics were developed to align to World Health Organization guidelines on the prevention of chronic disease.

A recent study suggests that composite metrics such as the GDR and the GDQS may have limited equivalence across contexts, and that they should be used with caution and ideally alongside their subcomponents. This is because consumption of healthy and unhealthy foods often co-occurs, reducing the discriminatory power of composite metrics.

IYCF-DQQ

A country-adapted IYCF-DQQ for infant and young child feeding has also been developed with an online indicator calculator, can calculate minimum diet diversity for this age group as well as other IYC indicators.

FV-GDR

The Fruit and Vegetable – Global Diet Recommendations score (FV-GDR), is an indicator that can easily be generated from the DQQ to measure fruit and vegetable consumption at the population-level. The FV-GDR has been validated against a 24-hour recall as a reference for FV intake and was correlated with actual).

Additional notes on indicators

Both the GDQS and the DQQ can be used to calculate other commonly used indicators of diet quality such as the Diet Diversity Scores (DDS) and the Minimum Dietary Diversity for Women (MDD-W) score. The DQQ has also been adopted by the DHS and Feed the Future to collect data on MDD-W.

The GDQS and the DQQ cannot be used to calculate the intake of specific nutrients that are over- or under-consumed in the population. Quantitative intake data on macro- and micronutrients is needed as well as the use of a food composition table and conversion factors, such as the data provided by a 24-hour dietary recall, weighed food record or quantitative food frequency questionnaire.

The DQQ cannot be used to calculate indicators such as the quantity of food groups consumed because it uses sentinel foods to assess presence or absence of a food group but not quantities (or frequencies) consumed.

TIP: Use standard DQQ for global benchmarking, and modified versions for local programmatic use: Adding or removing food groups changes the scoring structure and compromises comparability across countries, thus modified DQQs are typically not suitable for global comparisons. That said, if the same modified DQQ is used consistently over time in a country or region it can show trends or be used for program monitoring, policy evaluation, or seasonal comparisons

Tool and indicator validation

To ensure these indicators are reliable and meaningful, validation studies have been conducted across diverse settings and populations.
 
Validation is essential in determining the suitability of a dietary assessment instrument, focusing on its validity, misreporting and measurement errors. Validity assesses how accurately the instrument reflects actual intake, usually in comparison with other methods. Misreporting, influenced by factors like social desirability or memory limitations, can impact accuracy. Measurement errors, either systematic (bias) or random, affect the reliability of findings. Every dietary assessment method has its own set of potential biases and errors – no method is perfect.

The validity of the DQQ for collecting population-level food group consumption data to derive diet quality indicators compared to the 24HR recall (Uyar 2023). During its development, the DQQ was validated against dietary recommendations using data from the US and Brazil (Herforth et al 2020). The DQQ,  however, was not validated against measures of nutrient adequacy or NCD outcomes like other indicators of diet quality/diversity (DDS, MDD-W, and GDQS) were, using data from LMICs in Africa and Asia. The GDQS was also validated against several NCD outcomes (Hanley-Cook 2024).

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Sampling and data collection considerations

Regardless of resource level, thoughtful sampling and data collection strategies are essential to ensure representativeness and relevance of DQQ data.

The sampling approach depends on the user’s question of interest and target population, but it is crucial to ensure a study’s sample is representative of the target population. The two primary sampling approaches are probability and non-probability sampling. There are several methods of probability sampling, including simple random sampling, where any member of the target population has an equal chance of being selected into the study, interval sampling, in which people of the targeted group are continually available and selected into a sample (i.e., consumers in a market), and stratified sampling, which divides the target population into groups for sampling, and/or cluster sampling which uses groupings from which the sample population is selected.

In urban settings, administrative boundaries and enumeration areas can help organize sampling. In many countries, lists of enumeration areas can be acquired, after which a sample frame or list of households or targeted individuals from each of those areas are developed, from which households or individuals are sampled. Correcting for over- or under sampling through sample weighting is essential to improve data accuracy. If the question of interest is to assess changes at population-level in dietary quality due to a program or policy, it is critical that the sampling frame include populations that have been exposed to those interventions. Non-probability sampling methods, such as convenience and snowball sampling, can be used when ease of access is prioritized.

Careful conceptualization of the relationship between food environments and diets helps guide geographic focus and sampling strategy, ensuring more meaningful and representative results. For example, if your question of interest is to compare between areas of differing levels of urbanization, the geographic frame could include urban, peri-urban, and rural areas, and a sampling strategy would need to select a representative sample of households and individuals.

As the DQQ can be administered via different means such as in-person or phone, consider whether the method selected will capture populations that may be difficult to access (e.g., urban conflict-affected setting, populations without good phone service or access). The DQQ utilizes sentinel food lists and analyzes data at food group level but does not assess intake of nutrients that are under- or overconsumed. The DQQ and GDQS are not the best tools to use if you are interested in infrequently consumed foods. The DQQ can be administered via phone and has also been used with digital tools as a crowd-based system for high frequency data collection. In contrast, the GDQS app requires in-person interviews to facilitate the accurate estimation of quantities consumed at the food group level. For the DQQ, country adapted modules have been developed with context-specific sentinel food lists.

Other data sources

When primary data collection is not feasible, alternative data sources can complement or substitute DQQ-based assessments, though each comes with its own trade-offs.

While it is ideal to collect primary data, real world limitations to data collection in urban settings may prevent this, including on the implementing side (e.g., budget/resource constraints) and in the field (e.g., difficulty in accessing populations, conflict-affected settings). It may be helpful to examine secondary data sources, either as background to inform primary data collection or in place of it, if data collection is not feasible.

Data Sources	Pros	Cons	Indicators
Global Dietary Database [Individual-level]	-Most are nationally representative -Urban/rural residence included in half of surveys -Harmonized data (variables, units, food definitions) for individual-level dietary data from nutrition surveys for 188 countries	-Requires nutrition and data analysis expertise -Surveys use different designs and tools -Certain food categories are excluded (e.g., poultry, dairy-based desserts, highly processed or packaged foods, mixed dishes and recipes, condiments and spice, supplements)	-Includes 51 dietary factors based on public health and chronic disease risk (e.g., foods, beverages, nutrients, dietary patterns, or qualities)
GIFT Database (FAO)   [Individual-level]	-Data are disaggregated by sex and age but inconsistent urban/rural disaggregation -Individual quantitative food consumption data coded with the FoodEx2 European standardized food classification system to code foods for dietary and exposure assessments, data are screened and formatted using R -Dashboards presenting indicators and summary statistics on foods and diets -Can link food groupings to own dietary data (dataset available upon request)	-Need nutrition and data analysis expertise, particularly as outliers and missing data have not been removed from original datasets and energy and nutrient values are provided directly from surveys (does not link food consumption datasets to food composition data) -Many datasets are old, most not nationally representative and there are no data on statistical weights -Data covers only 36 countries (Africa: Benin, Burkina Faso, Cameroon, Ghana, Kenya, Malawi, Mali, Mozambique, Nigeria, Senegal, South Africa, Tanzania, Uganda; Asia: Bangladesh, Cambodia, China, India, Indonesia, Lao PDR, Nepal, Philippines, Thailand, Vietnam; Latin America: Brazil, Guatemala, Haiti, Mexico, Saint Kitts and Nevis; Europe: Albania, Armenia, Georgia, Moldova, Ukraine; Middle East/North Africa: Egypt, Lebanon, Morocco, Tunisia; Oceania: Fiji)	-Estimated usual intakes of selected nutrients (with SPADE tool) -MDD-W (and Food group diversity score, individual food group consumption) -Food consumption (daily diet g/per person per day, proportion of food groups consumed (%), calories per person per day) -Other indicators for food safety (dietary exposure to chemicals) and environmental impacts of food consumption (emission, water, and land use) -Statistics on food consumption can be calculated for individual food items or using food groups (e.g., sources of micro- and macronutrients in the diet, macronutrient contribution to total intake)
Demographic and Health Surveys [Individual-level]	–Nationally representative data on dietary diversity -Urban/rural disaggregation	-Need nutrition and data analysis expertise -Alternatively, the DHS StatCompiler and mobile app allows for automatic indicator calculation and disaggregation	-MDD-W -IYCF practices (MAD, MDD, MMF) -Percentage consuming ten food groups[1] (PLW, WRA)
Gallup World Poll (GWP) (Global Diet Quality Project) [Individual-level]	-Global coverage and standardization (140 countries, including those that lack nutrition surveillance data) -Integration with economic, social and health indicators -Frequent updates (every 5 years) -Samples adults aged 15+ (not just women)	-Other national surveys tend to align more closely with DHS than GWP. -GWP often collects data in lean seasons, potentially underestimating MDD-W compared to DHS. -Validating MDD-W for males aged 15–49 could expand GWP’s utility. –Greater variability in GWP estimates than DHS.	-MDD-W, DDS -All-5, protective, and unhealthy food consumption -Healthy diet pattern for NCD prevention -Zero fruit or vegetable consumption -Food groups
Household consumption and expenditure surveys (HCES) [Household-level food consumption]	-Implemented at scale often with national representativeness, but made financially viable through public and donor subsidies -Conducted some countries (every 3-5 years) with a large sample -Urban/rural disaggregation -Contains other variables such as data on socioeconomic status, education, and other determinants relevant to nutrition -Often also includes acquisition data (food acquired from purchases, production, in-kind)	-Household level food consumption, does not address intra-household allocation issues that may affect household members – not ideal source of dietary data. -Modules are heterogenous across countries, making comparisons challenging -Does not differentiate between subgroups to estimate differences in probability of deficiencies in high-risk groups -Often lacks information on types of foods consumed away from home (FAFH); the information on cost of meal consumed outside the home may be available, but not the type of foods/meals. This is a problem especially in urban settings where working HH members or school-age children often eat one or more meals outside the home.	-Food variety (Household diet diversity score) -Food consumption (Food consumption score) -Nutrient availability: macronutrient and micronutrient availability per capita per day (micronutrient availability requires use of FCT), per capita energy intake. -Consumption patterns (frequency or shares of animal-sourced foods, staple foods, ultra-processed foods), however, recall periods are variable (some foods reported over a week, others a month)

Illustrative research using these tools and indicators in urban settings

DQQ and Dietary Assessment-Related Resources

• Bailey, R., “Overview of dietary assessment methods for measuring intakes of foods, beverages, and dietary supplements in research studies.” Current opinion in biotechnology, 70 (2021), 91-96.
• Data4Diets: Building Blocks for Diet-related Food Security Analysis, Version 2.0. (2023). Tufts University, Boston, MA. https://inddex.nutrition.tufts.edu/data4diets. 
• Diet Quality Questionnaire. Infant and Young Feeding calculator. https://www.dietquality.org/iycf-calculator. 
• Diet Quality Questionnaire Indicator Calculator. https://www.dietquality.org/calculator.
• Diet Quality Questionnaire Tools and Country-adapted DQQs. https://www.dietquality.org/tools. 
• Food Systems Dashboard. Dietary diversity Score (Global Diet Quality Project/Gallup World Poll). https://www.foodsystemsdashboard.org/indicators/outcomes/dietary-intake/food-group-diversity-score/map.
• INTAKE. Center for Dietary Assessment. FHI 360. Accessed 2024. https://www.intake.org/resources.
• National Cancer Institute. Dietary Assessment Primer. National Institutes of Health (NIH). https://dietassessmentprimer.cancer.gov/approach/table.html

References