Nutrition Modeling Consortium
What is Nutrition Modeling?
Nutrition Modeling refers mostly to computer assisted optimization routines that seek to select a best solution (with regard to some nutrition objective) among a set of available policy or program alternatives. However, not all tools represented on the Consortium follow mathematical optimization algorithms. Some use spreadsheet formula, while other use qualitative methods to drill down on nutrition data. Regardless of their design, they all share the intent of improving the use of available knowledge and data in designing nutrition policies and programs.
The mandate of the Nutrition Modeling Consortium is to:
- Increase end-users’ input in the specification of services to be provided by these tools.
- Help end-users understand better how those tool can serve their nutrition programming needs.
- Advance the joint utility and effectiveness of the modeling tools through a collective effort by nutrition modelers in order to enhance their technical inter-operability, and improve their usability to end-users.
How the Consortium Works
The Consortium of Nutrition Modelers aims to improve the use of the evidence base in policy and program decision making through the use of mathematical modeling of nutrition activities. It is led by a Scientific Organizing committee composed of nutrition modelers and end-users. The Secretariat has the responsibility of linking the SOC to the modeling community and to end-users, to coordinate the work of the Consortium and to organize and convene meetings of the Consortium.
In April 2017, the Institute partnered with the Micronutrient Forum to convene a two-day technical consultation to review seven tools designed to help decision makers in low middle income countries (LMIC) develop and streamline their nutrition programs and interventions. These tools were selected on the basis of their ability to elaborate nutrition policy scenarios adapted to national priorities and contexts, whether through the use of mathematical optimization routines or other evidence-based analytical approaches. Services provided by those tools range from advocacy to allocative efficiency to budget planning. The meeting highlighted the deep capabilities of those tools, and several examples were presented of successful use that confirmed their potential utility to nutrition policy making. Yet their adoption by end-users in LMICs remains limited. To palliate for this, the New York Academy of Sciences was awarded a 2.5 year grant by the Bill & Melinda Gates Foundation to create a consortium for nutrition modeling aimed at improving the usability of tools, and at increasing their uptake by end users.
The Nutrition Modeling Consortium regroups personnel that support modeling tools, working at diverse institutions worldwide. It also includes in-country end users, such as Ministries of Health, Nutrition Departments, and implementers such as NGOs, or technical planners in-country. Membership to the Consortium is open to all stakeholders working in this area, provided they contribute to the group’s common goals.
Fill the Nutrient Gap / Cost of the Diet (FNG/CotD)
Intake Modeling and Prediction Program (IMAPP)
Lives Saved Tool (LiST)
Outcome Modeling for Nutrition Impact (OMNI)
MMS Cost-Benefit Tool
Cost of Not Breastfeeding
Micronutrient Action Policy Support (MAPS)
SEEMS-Nutrition Common Approach & Tools
Micronutrient Forum Global Conference, Bangkok, Thailand 2020
ANH Conference, 2020
Doing More with Less: Tools to Help Governments Optimize Nutrition Funding’s Impact - Webinar on Optima Nutrition and MINIMOD 2020
EAT Food Forum, Stockholm, Sweden 2019
SLAN Conference, Guadalajara, Mexico 2018
Optima Nutrition Training, Pretoria, South Africa 2018
African Nutritional Epidemiology Conference, Addis Ababa, Ethiopia 2018
Occasionally, members of the Consortium team up to jointly address special needs that are not yet covered by the group. Ongoing “work packages” are listed below:
Modeling the impact of Multiple Micronutrient Supplementation (MMS) on antenatal and postnatal outcomes
Led by the MMS taskforce, a module will be developed that allows to estimate, based on national data, the effect of switching from IFA to MMS in antenatal care programs on the following indicators: low birth weight; small for gestational age; pre-term birth; etc.).
Nutrition Theory of Change: Visualizer
Led by the LiST team at Johns Hopkins University, an interactive visualization framework is under development. The framework provides a visualization of the various impact pathways affecting nutrition outcomes, documenting the literature, datasets, assumptions, and much more. This means to elaborate further the UNICEF’s Nutrition Conceptual Framework. All consortium members participate in this initiative.
As a resource and communications hub for nutrition modeling tools, below is a repository of useful resources from consortium members and their respective tools, spanning from technical briefs, tool video tutorials, tool software, publications, and so on. To get started, pick an item from the table of contents below.
- Global Application of Nutrition Modeling Tools
- NMC Meeting Reports
- Case Studies
- Video Tutorials
- Mini Tool Clips
- User Manuals
- Data Sources
- Reports and Technical Briefs
- Peer Review Journal Articles
- SEEMS Common Approach & Tools
Fill the Nutrient Gap Tool
Fill the Nutrient Gap Ghana Summary Report
Fill the Nutrient Gap El Salvador Summary Report
Fill the Nutrient Gap Lao PDR Summary Report
Fill the Nutrient Gap Madagascar Summary Report
Fill the Nutrient Gap Pakistan Summary Report
Fill the Nutrient Gap Cambodia Summary Report
Fill the Nutrient Gap Tajikistan Summary Report
The Cost of Hunger in Ethiopia: Implications for the Growth and Transformation of Ethiopia
The Cost of Hunger in Malawi: Implications on National Development and Vision 2020
The Cost of Hunger in Uganda: Implications on National Development and Prosperity
Profiles Nutrition Advocacy by Country
- Economic Evaluation of Multisectoral Actions for Health and Nutrition
- SEEMS-Nutrition Generic Tools Overview
- Advocacy Brief Series
- Nurturing the health and wealth of nations: the investment case for breastfeeding
- CONBF Infographic
Daelmans B, Ferguson E, Lutter C.K, Singh N, Pachón H, Creed-Kanashiro H, Woldt M, Mangasaryan N, Cheung E, Mir R, Pareja R, Briend A. Designing appropriate complementary feeding recommendations: tools for programmatic action. Matern Child Nutr. 2013; 9(2):116-30.
Ferguson E.L, Darmon, N, Fahmida, U, Fitriyanti, S, Harper, T.B, Premachandra, I.M. Design of optimal food-based complementary feeding recommendations and identification of key "problem nutrients" using goal programming. J Nutr. 2006; 136(9):2399-404.
Ferguson EL, Watson L, Berger J, Chea M, Chittchang U, Fahmida U, Khov K, Kounnavong S, et al. Realistic food-based approaches alone may not ensure dietary adequacy for women and young children in South-east Asia. Maternal Child Health J. 2018; Sep 29. doi: 10.1007/s10995-018-2638-3. [Epub ahead of print].
Tharrey M, Olaya G.A, Fewtrell M, Ferguson E. Adaptation of New Colombian Food-based Complementary Feeding Recommendations using Linear Programming. J Pediatr Gastroenterol Nutr. 2017; 65(6):667-672.
Ferguson E, Chege P, Kimiywe J, Wiesmann D, Hotz C. Zinc, iron and calcium are major limiting nutrients in the complementary diets of rural Kenyan children. Matern Child Nutr. 2015: 11(3):6-20.
Walker N., Tam Y., Friberg IK. Overview of the Lives Saved Tool (LiST). BMC Public Health. 2013: 13(3):S1.
Walker N, Clermont A. Nutrition Modeling in the Lives Saved Tool (LiST), J Nutr. 2017:147(11).
Walker N, Friberg IK (Ed.s). The Lives Saved Tool in 2017: Updates, applications and future directions. Supplement in BMC Public Health. 2017: 17(4).
Walker, N. (Ed.). The Lives Saved Tool in 2013: new capabilities and applications. Supplement of BMC Public Health. 2013:13(3).
Fox M, Marterell R, Van Den Broek N, Walker N, (Ed.s). Technical inputs, enhancements and applications of the Lives Saved Tool (LiST). Supplement of BMC Public Health. 2011: 11(3).
R Pearson, M Killedar, J Petravic, JJ Kakietek, N Scott, KL Grantham, RM Stuart, DJ Kedziora, CC Kerr, J Skordis-Worrall, M Shekar, DP Wilson. Optima Nutrition: an allocative efficiency tool to reduce childhood stunting by better targeting of nutrition-related interventions. BMC Public Health. 2018: 18 (384).
Engle-Stone R, Perkins A, Clermont A, Walker N, Haskell M.J, Vosti S.A, Brown K.H. Estimating Lives Saved by Achieving Dietary Micronutrient Adequacy, with a Focus on Vitamin A Intervention Programs in Cameroon. The Journal of Nutrition. 2017: doi:10.3945/jn.116.242271.
Vosti S.A, Belinda R, Engle-Stone R, H. Luo. “Understanding Factors that Influence the Benefits and Costs of Rice Fortification” in Irizary, L., Prost, M-A., and D Murillo (Eds.), Scaling up Rice Fortification in Latin America and the Caribbean. Sight and Life and The World Food Programme. 2017: 176-181.
BakerS.K, Fracassi P, Kupka R, Neufeld L, M Shekar. Know Your Deficiencies, Know Your Response, Know Your Costs: A Commentary on Micronutrient Program Optimization Modeling. Food and Nutrition Bulletin. 2015:36(3).
Brown K.H, Engle-Stone R, Kagin J, Rettig E, S.A Vosti. Use of Optimization Modeling for Selecting National Micronutrient Intervention Strategies. Food and Nutrition Bulletin. 2015: 36(3).
Engle-Stone R, Nankap M, Ndjebayi A.O, Vosti S.A, K.H Brown. Estimating the Effective Coverage of Programs to Control Vitamin A Deficiency and Its Consequences Among Women and Young Children in Cameroon. Food and Nutrition Bulletin. 2015: 36(3).
Baldi G, Martini E, Catharina M, Muslimatun S, Fahmida U, Jahari AB, Hardinsyah, Frega R, Geniez P, Grede N, Minarto, Bloem MW, de Pee S. Cost of the Diet (CoD) tool: first results from Indonesia and applications for policy discussion on food and nutrition security. Food and Nutrition Bulletin. 2013: 34 (2).
Deptford A, Allieri T, Childs R, Damu C, Ferguson E, Hilton J, Parham P, Perry A, Rees A, Seddon J, Hall A. Cost of the Diet: a method and software to calculate the lowest cost of meeting recommended intakes of energy and nutrients from local foods. BMC Nutrition. 2017: doi 10.1186/s40795-017-0136-4.