Development of the Diet Quality Questionnaire for Measurement of Dietary Diversity and Other Diet Quality Indicators

Abstract

Background

To monitor trends toward healthy and sustainable diets, there is a need for feasible survey tools, with cross-cultural validity, low-cost, and low-expertise requirements.

Objectives

The objective of this research was to develop a method to gather data suitable for monitoring diet quality in the general population (women and men of all ages) that is feasible within multitopic surveys, low burden for both enumerators and respondents, valid at population level, and that captures the information necessary for understanding diet quality at global and local levels.

Methods

A literature review was conducted to identify constructs of diet quality with existing consensus, indicators with existing global demand, and methods that may be feasible and valid. Results were presented to a technical advisory group for debate, resulting in consensus on a set of constructs to be measured, desired indicators, viable data collection platforms, and an approach for testing and piloting.

Results

Food group-based indicators and 24-h recall period were selected as the most feasible and valid approach for population-level monitoring. A 29-item Diet Quality Questionnaire (DQQ) was developed, where each yes/no question asks about the consumption of a distinct food group on the previous day or night. The food groups were selected for the purpose of deriving indicators to capture the constructs for which there was consensus: nutrient adequacy, and protection against noncommunicable diseases, including both positive and negative risk factors.

Conclusions

The DQQ is low cost and feasible to administer in existing large-scale surveys, overcoming barriers to diet data collection that have precluded the routine monitoring of diet quality in the past. This novel approach has now been used across >85 countries in the Gallup World Poll and other surveys, generating the first nationally representative available datasets on Minimum Dietary Diversity for Women and complementary diet quality indicators.