3.1 Diet quality

Purpose of indicator

Eradicating food insecurity and malnutrition is a sustainable development goal (SDG2). Nutritious diets are a prerequisite to achieving this goal.

Key Metadata

Metadata Item Description
Indicator Name Diet quality
Theme Nutrition
SDGs Targeted SDG2
Data Source Household and individual survey using the Diet Quality Questionnaire (DQQ), developed by the Global Diet Quality Project
Measurement Diet quality from 24-hour food consumption recalls, calculated from the number of food groups consumed per individual and household, from 29 food groups.
Measurement Units Raw measurement: Y/N for consumption of each food group together with (optional) food group source (own production, purchased, borrowed, food aid, other). Final measurement: Household Food Group Diversity Score (FGDS) (0-10) (this measurement is the default KPI); Minimum Dietary Diversity for Women of Reproductive Age (MDD-W) (0/1); All-5 recommended food groups consumed (0/1); Non-Communicable Disease (NDC) Protect (0-9); NDC-Risk (0-9); Global Dietary Recommendations (GDR) Score (0 to 18); (Optional) Proportion of food that is produced on-farm

Guidance on Measurement

The Diet Quality Questionnaire (DQQ) is a standardized tool to collect indicators of dietary adequacy, including the minimum dietary diversity for women (MDD-W) indicator, and All-5, as well as indicators of protection of health against noncommunicable diseases (NCDs), including NCD-Protect, NCD-Risk, and the global dietary recommendations score (GDR). It is a low-burden tool for collecting valid, comparable food group consumption data in populations.

The DQQ is two-pages long and includes 29 multiple choice questions corresponding to the 29 DQQ food groups. Each question asks the respondent if they consumed a specific DQQ food groups with yes/no responses. The questionnaire takes approximately 5 minutes to complete per individual.

The survey should be completed for the adult household members, including men and women. Researchers can optionally add one column asking, for each food group, the source: own production, purchased, borrowed, food aid, other. This will allow calculation of the proportion of consumed food that is from each source.

Note that the timing of survey affects results. Try to collect data from all farms at a period when food supplies are adequate, rather than when some farms are more likely to suffer shortages (e.g. end of dry season, or after a drought season).

DQQ Food Groups:

  1. Foods made from grains
  2. Whole grains
  3. White roots, tubers, and plantains
  4. Pulses
  5. Vitamin A-rich orange vegetables
  6. Dark green leafy vegetables
  7. Other vegetables
  8. Vitamin A-rich fruits
  9. Citrus
  10. Other fruits
  11. Baked / grain-based sweets
  12. Other sweets
  13. Eggs
  14. Cheese
  15. Yogurt
  16. Processed meats
  17. Unprocessed red meat (ruminant)
  18. Unprocessed red meat (non-ruminant)
  19. Poultry
  20. Fish and seafood
  21. Nuts and seeds
  22. Packaged ultra-processed salty snacks
  23. Instant noodles
  24. Deep fried foods
  25. Fluid milk
  26. Sweet tea / coffee / cocoa
  27. Fruit juice and fruit-flavored drinks
  28. Sugar-sweetened beverages (soft drinks, energy drinks, sports drinks)
  29. Fast food

Guidance on Data Entry and Reporting

no information is available

Calculation Method


  tmp <- main_surveys %>%

    ## calculate main food group presence from sub-groups
    mutate(
      grains = ifelse(baked == 1 | grains == 1 | tubers == 1, 1, 0),
      pulses = as.numeric(pulses),
      nuts_seeds = as.numeric(nuts),
      dairy = ifelse(cheese == 1 | yogurt == 1 | milk == 1, 1, 0),
      meats = ifelse(
        processed_meats == 1 |
          red_meat_ruminant == 1 |
          red_meat_non_ruminant == 1 |
          poultry == 1 |
          seafood == 1,
        1,
        0
      ),
      eggs = as.numeric(eggs),
      dark_leafy_veg = as.numeric(darkgreen),
      vitA = ifelse(vita_veg == 1 | vita_fruit == 1, 1, 0),
      veg = as.numeric(otherveg),
      fruit = ifelse(citrus == 1 | otherfruit == 1, 1, 0)
    ) %>%
    rowwise() %>%
    mutate(
      ## indicator is sum of main food groups

      kpi15_diet_diversity = sum(
        c_across(c(
          grains,
          pulses,
          nuts_seeds,
          dairy,
          meats,
          eggs,
          dark_leafy_veg,
          vitA,
          veg,
          fruit
        )),
        na.rm = TRUE
      )
    )

  ### include the new variables in the performance_indicators data frame
  performance_indicators <- performance_indicators %>%
    left_join(
      tmp %>% select(farm_id, owner_id, submission_id, kpi15_diet_diversity)
    )

Indicator Interpretation and Threshold Setting

no information is available

Limitations

no information is available

References

Global Diet Quality Project resources: https://www.dietquality.org/

Diet Quality Questionnaires are adapted to each country, downloadable from the Global Diet Quality Project website and translated into many languages.