Recurring posts and progress updates on MNDA pilot, as well as other TCi fieldwork efforts, will continue as the summer progresses… check back in with us!
In a recent post, I discussed the how the Tata-Cornell Agriculture and Nutrition initiative (TCi) is advancing work on The Minimum Nutrition Dataset for Agriculture (MNDA). The essential metrics outlined in the MNDA will offer a standardized and streamlined way to measure nutrition status within current and existing agriculture surveys. The final outcome will be a ~2 page addendum of the most essential nutrition metrics that can give a nutritional ‘snapshot’ of individuals living in rural areas of the developing world. It will be easily inserted into existing agriculture surveys and useful tracking long-term changes in nutrition and comparing across datasets and interventions.
Next week the TCi team, along with our partner organization, ICRISAT, will pilot test 1 of the 5 modules of the MNDA in Andhra Pradesh and Maharashtra. This includes the individual, household and market-level dietary diversity scoring segment (module 3). Throughout the summer in India, TCi staff and student interns we’ll refine the survey instruments, pilot test the use of focus groups and surveys, and begin to compare findings with the intensive nutrition survey undertaken recently by ICRISAT for the same villages and households.
Capturing Individual and Household Dietary Diversity in the MNDA
For the purposes of the MNDA, our mission is to select the most essential nutrition metrics useful for linking agricultural changes and interventions to nutritional outcomes. We measure dietary diversity because rural households, who disproportionately make up the majority of stunted, wasted, and undernourished people, find their food and income from farming. Income on the farm contributes to household food budgets, and local food supplies are affected by local production patterns. Lack of dietary diversity is a particularly severe problem among poor populations in the development world because diets are dominated by starchy staples and grains, with little or no animal products and few fresh fruits and vegetables (Gómez & Ricketts, 2013; Gómez et al., 2013; Ruel, 2003; Tontisirin, Nantel, & Bhattacharjee, 2002). Accurately assessing a household and individuals access to a diverse diet is a key measurement area useful for identifying how food insecurity can contribute to malnutrition. Our dietary diversity module captures 3 elements:
- 2 Dietary Diversity Scores (including a household-level score identifying household access, and an individual score for micronutrient access for women)
- Focus groups for understanding local diets and correctly classifying local foods
Using Household and Individual Dietary Diversity Scores (FVCs)
The MNDA approach is to ask the primary household cook (often the daughter, daughter-in-law, or female head of house) who is between 15-45 years. The MNDA asks what she ate and cooked over the past 3 days as a proxy for overall household nutrition. This means the MNDA captures a rough idea of food consumption at the household level and for a specific individual woman within her childbearing years. All foods recalled are collapsed into major food groups, as outlined in the FAO/FANTA Dietary Diversity Guide. A household-level dietary diversity score (HDDS) is comprised of individual foods that have been collapsed into major and minor food groups. To determine an individual score for women, the MNDA identifies a second score by collapsing individual foods into a different list of micronutrient-rich foods that are high in iron and minerals (women’s dietary diversity score or WDDS). Thus the MNDA offers 2 types of dietary diversity indicators; a HDDS which identifies access to a broad range of food groups, and a WDDS based food groups that are micronutrient and protein-rich. These kinds of food-group scores have remained popular because of their simplicity and field- feasibility (Ruel, 2003).
Reference (recall) range from 1-15 days, but many collect dietary information over 1-3 days periods. While 24 hour recall periods are thought to have greater levels of accuracy because they do not require the respondent to recall consumption information beyond one day, research has shown that the number of different foods consumed increases with time but plateaus at 15 days (Drewnowski, Ahlstrom-Henderson, Driscoll, & Rolls, 1997). A recent review of dietary diversity measurement issues by Ruel (2003) suggests that a 3 day recall period can reasonably estimate much of the variation of an individuals diet.
The MNDA is piloted with a 3-day recall period plus the ‘market day’ (Leftmost image). We elected to pilot test the inclusion of the local outdoor market day for several reasons. First, the household/individual is likely to purchase and prepare perishable foods (like meats) on that day, suggesting that lack of inclusion might underestimate purchase or consumption of critical food groups. Second, an individual may weekly consume diverse snacks, treats, or other “special foods” on this day that might otherwise go uncounted. Third, and perhaps most importantly, inclusion of the market day can provide an important weekly ‘anchor’ for respondent recall accuracy. Finding appropriate “anchors” and points of reference for respondents has been found to be critical for dietary recall (Subar et al., 1995).
Using Focus Groups to Ground Assumptions and Ensure Proper Food Classification
Since the HDDS and WDDS are scores based on food groups, identifying classifying specific foods into the correct groups is essential. This means understanding local diets contextualizing responses. We are additionally piloting the use of focus groups prior to individual surveying in order to identify and validate necessary minor and major food groups. We undertake this effort to collect a picture of local diets, commonly consumed foods, discuss specific foods or the components of major dishes that are frequently prepared. Focus group discussions on food groups will begin with the 16 food groups outlined by the Food and Agriculture Organization (FAO) (Kennedy, Ballard, & Dop, 2011).
…more updates soon on our pilot progress!
Drewnowski, A., Ahlstrom-Henderson, S., Driscoll, A., & Rolls, B. (1997). The Dietary Variety Score. Journal of the American Dietetic Association, 97(3), 266–271. doi:10.1016/S0002-8223(97)00070-9
Gómez, M. I., Barrett, C. B., Raney, T., Pinstrup-Andersen, P., Meerman, J., Croppenstedt, A., … Thompson, B. (2013). Post-green revolution food systems and the triple burden of malnutrition. Food Policy, 42, 129–138. Retrieved from http://www.sciencedirect.com/science/article/pii/S0306919213000754
Kennedy, G., Ballard, T., & Dop, M. (2011). Guidelines for measuring household and individual dietary diversity (pp. 1–60).
Ruel, M. T. (2003). Operationalizing Dietary Diversity: A Review of Measurement Issues. Journal of Nutrition, 133(11), 3911S–3926S.
Subar, A., Thompson, FrancesSmith, A., Jobe, J., Ziegler, R., Potischman, N., Schatzkin, A., … Harlan, L. (1995). Improving Food Frequency Questionnaires. Journal of the American Dietetic Association, 95(7), 781–788. doi:10.1016/S0002-8223(95)00217-0
Tontisirin, K., Nantel, G., & Bhattacharjee, L. (2002). Food-based strategies to meet the challenges of micronutrient malnutrition in the developing world. The Proceedings of the Nutrition Society, 61(2), 243–50. doi:10.1079/PNS2002155