1. Ask people to look at the 2 pager for more information on MERIAM

2. How can we predict malnutrition crises? And why do we need to??
Thank Dr. Adesina…. When I realized how this breakfast was being set up - I was not quite sure how I was going to consolidate 200 pages of literature review and data landscaping into a 7 minute presentation at 8am to a such a diverse audience. But by focusing on how Action Against Hunger is thinking about nutrition early warning and why it has proved so challenging for the food security and nutrition communities to do so up this point, I think that we can set the stage for the discussion going forward….
Allison Greenberg
Action Against Hunger
AGRF 2017 – CIAT Thought Leadership Breakfast

3. Malnutrition crises are complex.
Nutrition crises are complex, how do we know which causal factors are the most important? Are the causal factors in one village the same as in the next? What about in different districts, regions, countries… ? The causes are hard to generalize and change in different contexts and settings. How do you choose what is important among the noise- how do you bring in local context to this and how do we measure it?
Source: LinkNCA West Pokot County, Kenya 2015 (Action Against Hunger, West Pokot County Government)

4. Undernutrition causal frameworks tend to not capture the complexity of feedback loops, household behavior, and shocks
How can a model account for positive and negative shocks, each with their own pathway?
How can models capture disease as a cause and effect of malnutrition? And which diseases?
How can household decision making around using resources be understood and incorporated?
To date, efforts to boil this down have not quite captured the complexity that exists…..
When do conflict events cause malnutrition? How do you analyze changing climate trends?
Source: UNICEF Causal Framework for Malnutrition

5. Food security early warning (EW) systems only capture half the picture
IPC is the standard analytical framework for food security EW
Only looks at health and sanitation as contributing factors
Nutrition status is a LATE indicator of crisis- helps establish current situation and trends, but not for prediction
Most existing early warning systems are focused on food insecurity, which can been a proxy for nutritional security, but does not fully analyze nutrition causal factors that are not directly related to food consumption.
Although related, FS outcomes are not the same as nutrition outcomes- which are analyzed here only in terms of FS
Source: Integrated Phase Classification (IPC) Manual 2.0

6. Existing monitoring systems are not always effective for nutrition EW
EARLY WARNING, SURVEILLANCE AND MONITORING INFORMATION SYSTEMS
 EXAMPLES IN MERIAM PRIORITY COUNTRIES
ETHIOPIA
Bi-annual SMART surveys in vulnerable areas ; LIAS = Livelihood Impact Analysis Sheet; Information system administered by ENCU; National early warning system administered by EWR Directorate, DRMFSS, MoA  
KENYA
Bi-annual SMART surveys in vulnerable areas; National Nutrition Information System; Surge model; Integrated food security and nutrition sentinel surveillance system; Kenya Drought Early Warning Systems
MALI
Annual SMART surveys; Analyse de l’économie des Ménages (HEA); Système d’Alerte Précoce (SAP)
NIGER
Annual SMART surveys; Système communautaire d’alerte précoce et de réponse aux urgences (SCAP/RU); Système d’Alerte Précoce (SAP)
NIGERIA
SMART surveys - Annual for national and bi-annual in NE Nigeria ; Nutrition Surveillance in NE Nigeria
SOMALIA
Bi-annual SMART surveys in vulnerable areas; Rapid food security assessments using Participatory Rapid Assessment (PRA) techniques; Food Security and Nutrition Information System administered by the FSNAU; BRCiS and SomReP monitoring, part of resilience programming
SOUTH SUDAN
Rapid and Bi-annual SMART surveys in vulnerable areas; Food Security and Nutrition Monitoring system; National Nutrition Information System; National Early Warning system
UGANDA
Karamoja nutrition surveillance (stopped); Karamoja Drought Early Warning System 
SELECT GLOBAL EXAMPLES
BANGLADESH
Food Security and Nutrition Surveillance Project 
BOTSWANA
Clinic based monitoring
BURKINA FASO
Listening posts project 
DRC
Rapid Response to Nutrition Crisis Project
MALAWI
Integrated Nutrition and Food Security Surveillance System (stopped); mVAM (mobile Vulnerability Analysis and Mapping)
NAMIBIA
Namibia Food and Nutrition Security Monitoring
PHILIPPINES
 Early Warning System for Food and Nutrition Security
TIMOR-LESTE
 National Information and Early Warning System
REGIONAL
 CILSS Information and Early Warning System; SADC AIMS system
mVAM (mobile Vulnerability Analysis and Mapping) in West Africa
INTER-NATIONAL
Integrated Phase Classification (IPC); FEWS NET Information System; GIEWS;
Many efforts focused on food security using the IPC framework
Many poorly resourced efforts with long delays for data collection and analysis
Many nutrition and food security information and monitoring systems exist, but there are a lot of issues with being able to use them for effective nutrition early warning
Many efforts focused solely on one factor such as climate, agricultural production, or market prices
Source: MERIAM Literature Review

7. Existing nutrition surveillance systems have many inefficiencies
Our review found that there is often:
Big questions around data quality
Missing local, contextual explanations for trends
Delays in analyzing data and communicating findings
A disconnect between technicians analyzing data and decision makers planning a response
A lack of political will/ understanding on how to use surveillance and early warning information
Cross sectional surveys Examples: FSNSP, Bangladesh; Annual SMART, Nigeria; Karamoja Surveillance Program, Uganda
Community-based sentinel sites
Examples: FNSM, Namibia; Listening Posts, Burkina Faso; mVAM, Malawi
Sequence
Example: RRCN, DRC
Institutional sources
Examples: MoH Surveillance Botswana; INFSS, Malawi; Surge Model, Kenya
Program data
Example: School feeding programs
Combination of methods/ sources
Examples: FEWS NET; CILSS, West Africa; NIEWS, Timor- Leste; EWS, Ethiopia; EWS- FNS, Philippines
Primary Data Sources/ Sampling
Secondary Data Sources/ Sampling
Nutrition Surveillance
Nutrition surveillance programs tend to be plagued with inefficiencies, a lack of resources and data quality issues that some times make it difficult to for timely reporting that captures the big picture analysis of why the nutrition situation is improving or declining and which is often disconnected from policy makers who are deciding where to send resources
Other Methods
Examples: mVAM (mobile technology, mixed sampling); BRCiS and SomReP, Somalia
Source: MERIAM Literature Review

8. So, can we use other data sources and approaches to more effectively build on existing systems to monitor and predict malnutrition crisis?
Governance
Afrobarometer (available here)*
U Maryland Polity Regime Classification
The Ibrahim Index of African Governance
UN Habitat Urban Governance Index
World Bank Governance Indicators
World Bank Low Income Countries Under Stress (LICUS) Indicators
The Failed States Index
Corruption Perception Index
Development
Night Lights Data (VIIRS/DMPS-OLS)*
Human Development Index (HDI)
Travel time to major cities (available here)*
Road networks
Living Standards Measurement Study (LSMS)*
The Social Vulnerability Index (SoVi)  
World Bank Development Indicators
Foreign Aid
Geocoded data on development/humanitarian projects ( )
Geocoded data on US-implemented aid projects ( )
FAIS high level national summaries of aid received (available here)
WFP Food Aid Shipments (available here)*
Health and Demographics
Ethnic Power Relations (EPR)*
Gridded Population of the World (GPW)*
LandScan (available here)*
Demographic and Health Survey (DHS)*
Multiple Indicator Cluster Survey (MICS)*
Administrative Boundaries & Spatial Grids
Global Administrative Areas (GADM)*
Peace Research Institute Oslo GRID data (PRIO-GRID)*
Disaster and Environmental Vulnerability
The Emergency Database (EM-DAT)
Global Risk and Vulnerability Index
The Prevalent Vulnerability Index Index
The Environmental Vulnerability Index
The Environmental Sustainability Index 
Nutritional Security
Standardized Monitoring & Assessment of Relief & Transitions (SMART): Rapid SMART*; SMART National Nutrition Surveys (NNS);
Nutrition Surveillance Programs (Action Against Hunger)*
Nutrition Causal Analysis (Link NCA)*
National and District Clinic Based Surveillance
Environmental and Agricultural Remote Sensing
Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS)*
Soil Moisture for Africa (available here)
Vegetation datasets (MODIS)*
Land cover data (available here)*
WRSI crop model data (available here)*
MapSPAM (available here)*
Pastoral Early Warning System (PEWS)
Food Prices and Related Economic Data/Indices
FAO Global Information and Early Warning System (GIEWS)*
FAO food price tool (available here)
FAO Domestic Food Price Level Index (DFPLI)
FAO Consumer Prices General Index (CPI)
Famine Early Warning Network market data (FEWSnet)*
WFP food price tool (available here)
Africa food prices.io (available here)
ILO Commodity Price Data (available here)*
ILO Food Price Index (available here)
Climate Change, Agriculture, and Food Security (CCAFS)
Conflict
UCDP Georeferenced Events Dataset (GED)*
Social Conflict Analysis Dataset (SCAD)*
Armed Conflict Location and Event Dataset (ACLED)*
Global Terrorism Database (GTD)*
Global Database of Events, Language, and Tone (GDELT)*
Integrated Conflict Early Warning System (ICEWS)*
Geocoded Peacekeeping Operations Dataset
Territorial Control in Civil Wars (available here)*
YES!! There is so much existing data and information out there - we need to focus not only on strengthening and using information from existing systems- but also pulling in complementary data that can provide the big picture and captures the complexity
Source: MERIAM Data Landscape Report

9. The MERIAM approach uses existing data to identify the most important causal factors (econometric) and understand community level resilience (computational)
Three level econometric model to combine regional, GIS, and household data on independent variables that effect malnutrition (wasting). This will seed the computational model….
Agent based computational model will build household evaluation functions that measure coping strategies and household resilience curves to model the nutritional landscape of a community
We want to understand which districts are most at risk of acute malnutrition (wasting) and understand what interventions would be needed to stop a crisis BEFORE it happens. And it’s not only about the MERIAM approach, its about how we can use new technology, data sources, and methodologies to analyze nutrition early warning more effectively, in a way that captures the complexity of a nutrition causal factors and allows for stakeholders at all levels to both input into system information and receive the information when they need it most
Source: MERIAM Data Modeling Plan

10. And if we can develop better nutrition early warning, how can we be sure that decision-makers and program planners will use the information and that communities at risk will receive it?
The MERIAM project’s Research Uptake strategy looks to involve stakeholders at all steps of the research project in order to validate and ground truth the models and ensure primary stakeholders build a sense of ownership over the research
It’s a diverse stakeholder landscape that needs to come together to collaborate, be transparent about data, and apply the best technology. The only way we can possibly achieve this is by working together and persistently so that nutrition early warning is accurate and relevant. It is just the beginning of a long conversation that will require the expertise and resources of everyone in this room and beyond…
Source: MERIAM Research Uptake Strategy