Minimum Dietary Diversity for Women (MDD-W)

This course has been designed to explain how to use the Minimum Dietary Diversity for Women (MDD-W) indicator, with a view to contributing to improved diets among nutritionally vulnerable women of reproductive age.

Topics

SDG
SDG2: Zero hunger
Subject
Minimum dietary diversity for women
Keywords
Food and nutrition security; health

The course titled "Minimum dietary diversity for women" was developed by FAO’s Nutrition Assessment Team in collaboration with the FAO e-Learning Academy. The course explains how to use the MDD-W indicator with a view to improving diets among nutritionally vulnerable women of reproductive age. It is aimed at professionals in organizations whose programmes have impact pathways aiming to improve dietary diversity, in particular policy advisors, programme or statistics officers, academics and researchers. The course is expected to take 2-3 hours to complete and contains four lessons which gradually develop understanding of MDD-W and dietary diversity: Lesson 1, Introduction to MDD-W; Lesson 2, The food groups and data collection methods; Lesson 3, Field work preparation; and Lesson 4, MDD-W calculation and presentation. The course combines knowledge-based slides, interactive graphics, examples, exercises and tests. Upon completion, a digital certificate can be obtained by passing a final test.

Target Audience

This course is primarily intended for members of institutions and organizations whose (nutrition-sensitive or nutrition-specific) work has pathways aiming to improve dietary diversity, as well as for people with an interest in the process, including:

  • Policy-advisors
  • Programme officers
  • Statistics officers
  • Academics and researchers
  • UN agencies
  • Non-governmental organizations

Learning Objectives

  • The importance of simple, gender-sensitive, food-based indicators, such as MDD-W, to assess nutrition-related outcomes.
  • How to use MDD-W to identify vulnerable groups of women.
  • The skills needed to collect, analyse and interpret MDD-W data.

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