EAC Statistics E-Learning Resources · Food and Nutrition Security
EAC Statistics E-Learning Resources
A plain-language, slide-by-slide course on Food and Nutrition Security (FNS) in the East African Community — what it means, the four pillars that hold it up, the tools used to measure it, and the EAC framework that brings it all together. Grounded in the global FNS evidence base and the EAC regional context.
This online material explains Food and Nutrition Security in plain language and shows why it matters across the East African Community. It moves from foundations — what FNS is and the types of food insecurity — through the four pillars of availability, access, utilization and stability, the household and nutrition assessment tools used to measure them (IPC, FCS, HDDS, CSI, HHS, FIES, anthropometry and IYCF), the EAC FNS assessment framework and its indicators, and finally how the pieces connect into a single, harmonised regional system.
What food and nutrition security means, the global and EAC context, and the types of food insecurity.
Availability, access, utilization and stability — how each pillar works and how they depend on one another.
The IPC, Food Consumption Score, dietary diversity, coping-strategy and hunger scales used to measure food security.
Anthropometric measures, MUAC, and infant and young child feeding (IYCF) indicators, with survey design and data quality.
The regional assessment framework, its core indicators, and how they map to the four pillars and global goals.
Overcoming fragmentation — the institutional architecture and harmonised methodologies that tie the system together.
A shuffled final assessment drawn from a question bank, with a downloadable certificate on success.
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FNS · work through the modules, then take the final assessment
Each module is a short slide deck — open one, move through it slide by slide with Next and Previous, and return here to pick the next. A final assessment draws on all the modules; pass it to earn your certificate.
Mastering Food and Nutrition Security: The EAC Framework
From Fragmentation to Harmonization: Building a Unified Regional FNS Monitoring System
A shuffled set drawn from a large question bank — pass to earn your EAC certificate.
Mastering Food and Nutrition Security: The EAC Framework
Mastering Food and Nutrition Security: The EAC Framework
Experience the daily reality of food insecurity in the East African Community
Imagine you're a mother in rural Burundi. You wake at dawn to fetch water—a three-hour journey. Your children need breakfast, but the maize harvest failed due to drought. Food prices have doubled. Your youngest shows signs of malnutrition: thin arms, a distended belly, listless eyes. You must choose: buy food or pay for medicine?
This isn't imagination—it's reality for millions across the East African Community. Right now, as you take this course, over 90 million people in our region face some level of food insecurity. One in three children is stunted, their physical and cognitive development compromised before they reach age five.
But here's the powerful truth: food and nutrition insecurity is not inevitable. With the right knowledge, data, and coordinated action, we can change this story. That's why you're here.
Let's unpack this definition—every word matters. Understanding each component is essential for effective monitoring and intervention.
Food Security refers to availability and access to food, while Nutrition Security ensures that food provides adequate nutrients and that people's bodies can absorb and use those nutrients effectively.
Example from Burundi: Maize might be available in markets (food security), but if children suffer from diarrhea due to unsafe water, preventing nutrient absorption, they experience nutrition insecurity even when food is present. Both dimensions must be addressed together.
Let's examine the sobering reality of where we stand globally and in our region. Understanding the magnitude of the challenge is the first step toward solving it.
Globally, an estimated 673 million people faced hunger in 2024 — down from the 2021 peak of about 811 million, but still above pre-pandemic levels. Progress is uneven: hunger continues to rise in Africa, where the prevalence of undernourishment has now surpassed 20%. Child stunting has improved globally to 23.2% (150.2 million children) in 2024, yet remains far higher across much of the EAC.
Our region comprises seven countries: Burundi, Democratic Republic of Congo, Kenya, Rwanda, South Sudan, Tanzania, and Uganda. Together, we're home to over 343 million people—a population growing at 2.9% annually, one of the highest rates in the world.
With 34.9% of our population living below the international poverty line, millions cannot afford adequate food. In Uganda in 2022, when food prices spiked, families reported skipping meals or reducing portion sizes—the painful trade-offs of poverty.
South Sudan's ongoing civil conflict has displaced millions, disrupting agricultural production and markets. In DRC, armed groups control key agricultural areas, preventing farmers from accessing their land. Conflict doesn't just destroy crops; it destroys entire food systems.
Drought events have increased by 30% since 2000. Kenya's 2022 drought left 4 million people needing assistance. South Sudan's 2020 floods affected 1 million. The 2020 locust invasion in Kenya destroyed crops across 70,000 hectares. Our region faces increasingly unpredictable weather.
⚠️ These factors don't operate in isolation. A farmer experiencing drought (climate) may lose their livelihood (poverty), and in a country with weak governance, this can contribute to conflict over scarce resources. It's a vicious cycle—but cycles can be broken through evidence-based action.
Food insecurity isn't one-size-fits-all. Understanding the different types is essential because each requires different assessment approaches and interventions. The EAC Framework recognizes three main types:
| Aspect | Chronic | Transitory | Acute |
|---|---|---|---|
| Duration | Long-term (years/generations) | Short-term (weeks to months) | Sudden (days to weeks) |
| Causes | Structural: poverty, inequality, poor infrastructure | Seasonal or temporary shocks: drought, price spikes | Emergencies: conflict, disasters, economic collapse |
| Recovery Capacity | Low without intervention | Moderate; can bounce back | Requires external assistance |
| EAC Example | 🇧🇮 Burundi: 50% child anemia rate persisting for years | 🇹🇿 Tanzania: 2022 drought affecting one harvest season | 🇸🇸 South Sudan: 2023 crisis with 7M in Crisis phase |
| Response Needed | Long-term development: agriculture, health, education | Medium-term support: cash transfers, agricultural inputs | Emergency aid: food distribution, therapeutic feeding |
| Assessment Type | Comprehensive (DHS, Food Consumption Surveys, HDDS) | Either, depending on severity | Rapid (IPC, CARI, HEA) |
These types are interconnected. Acute crises, if not addressed, can become chronic. When South Sudanese families sell their farming tools to buy food during an acute crisis, they lose the means to produce food next season—sliding into chronic insecurity.
Similarly, chronically food-insecure households are more vulnerable to shocks. A Burundian family already eating inadequate diets has no buffer when drought strikes—a transitory event quickly becomes acute for them.
In rural Burundi, where 50% of children suffer from anemia, this is chronic food insecurity. Families may produce enough calories from beans and cassava, but their diet lacks essential micronutrients—iron, vitamin A, zinc.
Year after year, generation after generation, malnutrition persists. Children grow up stunted, affecting their lifelong earning potential, which perpetuates the cycle.
→ Requires long-term solutions: improving agricultural productivity, strengthening health systems, enhancing education, building roads to markets, empowering women.
During Tanzania's 2022 drought, normally food-secure households suddenly faced crop failures. Maize prices spiked 30%. Families coped by reducing meal frequency, eating less preferred foods, even selling small livestock.
But when the rains returned and the next harvest came, most households recovered their food security.
→ Requires short-to-medium term responses: cash transfers, input subsidies to replant, water trucking. Goal: prevent temporary shocks from becoming chronic by depleting assets.
In 2023, IPC analysis classified 7 million people—over 60% of South Sudan's population—in Crisis (Phase 3) or worse levels of acute food insecurity. Conflict disrupted farming and markets. Floods destroyed crops. Hyperinflation made food unaffordable.
People were literally at risk of starvation.
→ Requires immediate humanitarian response: emergency food distributions, therapeutic feeding for malnourished children, protection of livelihoods to prevent total collapse.
The answer is an emphatic YES!
But only if our actions are guided by evidence
Food and nutrition security monitoring is the systematic collection, analysis, and use of data to understand:
Without this evidence, we're flying blind—wasting resources on ineffective programs while the truly vulnerable remain invisible.
Each EAC Partner State was conducting FNS assessments, but using different methodologies, collecting different indicators, at different times. Kenya used IPC tools for rapid assessment. Tanzania relied heavily on DHS surveys. Rwanda had strong nutrition surveillance. But the data wasn't comparable. Regional reporting was fragmented. Donors couldn't see the full picture.
The EAC Secretariat recognized that to achieve the vision of the Food and Nutrition Security Policy (adopted in 2016)—to attain FNS for all 343 million people of the EAC throughout their life cycle—we needed harmonization.
Aligned with SDGs, CAADP, and WHO targets. Covering all four pillars: availability, access, utilization, and stability.
For comprehensive assessments (chronic FNS) using DHS, surveys, and surveillance systems, AND rapid assessments (acute FNS) using IPC, CARI, and HEA.
Coordination mechanisms so governments, NGOs, research institutions, international partners, and communities work together.
Ensuring the evidence collected actually informs policy and programs.
This course will equip you to implement this Framework. By Module 9, you'll be ready to conduct assessments, analyze data, coordinate stakeholders, and use evidence to drive action in your country and across the region.
Scenario: Markets overflow with food. Maize, beans, vegetables—all available.
Reality: Families sit hungry because they cannot afford to buy.
One in five people face severe constraints in accessing food despite physical availability.
Scenario: Families have money in hand, savings from better times.
Reality: Markets are empty—conflict has disrupted supply chains.
Money is useless when there's nothing to buy.
Same outcome—hunger. Completely different causes. This is why we need the Four Pillars framework.
Is enough food produced or imported?
Can people obtain the available food?
Can people's bodies use the food for good health?
Do these conditions hold over time?
These four pillars provide the foundation for comprehensive food security analysis in our region. Each pillar is essential, has specific indicators we must monitor, and critically—they're interconnected.
Food availability is the physical presence of food, whether from domestic production, imports, or food stocks, sufficient to meet the aggregate consumption requirements of a population.
What we grow ourselves - the foundation of food availability in most EAC countries.
What we bring in from elsewhere - critical for addressing production gaps.
What we've stored from previous harvests - essential for stability.
Rwanda's Rice Situation:
Calculate:
Access is the ability of individuals, households, and communities to acquire adequate quantities of food through legal, economic, social, and physical means.
Can you afford food?
Can you reach food?
Does your identity limit access?
Do you have rights/entitlements?
| Country | Severe Food Insecurity (%) | Year |
|---|---|---|
| South Sudan | 63.1% | 2024 |
| DRC | 42.5% | 2024 |
| Kenya | 28.1% | 2024 |
| Tanzania | 24.4% | 2024 |
| Burundi | 20.9% | 2024 |
| Uganda | 14.9% | 2024 |
"A 10 percent increase in food prices is associated with a 3.5 percent rise in moderate or severe food insecurity."
During 2021-2023, low-income EAC countries experienced food price inflation peaking at 30% in May 2023—the highest globally.
Market vendor Nakato saw maize meal prices rise from 3,000 to 4,800 UGX per kilo—a 60% increase. Her family's monthly income remained constant at 25,000 UGX.
Result: Previously buying 15 kg monthly, she could now afford only 9 kg. Access deteriorated by 40%. Family reduced from three to two daily meals.
Nearest functioning market is 30-40 km away. Roads are unpaved and impassable during rains.
In northern Kenya, cultural norms around food and gender create constraints. Women may be excluded from decisions about selling livestock—the primary asset.
Impact: Women's specific nutritional needs (pregnancy, lactation) may not be prioritized in food acquisition decisions.
In settlements like Kakuma (Kenya) or Nakivale (Uganda), legal restrictions limit livelihood options:
Food utilization refers to the use of food through adequate diet, clean water, sanitation, and health care to reach a state of nutritional well-being where all physiological needs are met.
Critical Insight: You can eat adequate food yet suffer malnutrition if the food lacks essential nutrients, is contaminated, or your body cannot absorb nutrients due to disease.
EAC average: ~35% (one in three children has compromised physical and cognitive development)
Global average: 23.2% | 2030 SDG target: 14%
SOFI 2025: "This has lifelong consequences—stunted children earn 20% less as adults, perpetuating poverty across generations."
Adequate quantity and quality must be consumed
Can break if: Inadequate availability/access, poor dietary diversity, food taboos, intra-household inequalities
Food must be free from contamination
Can break if: Aflatoxin contamination (55% of Nairobi maize samples exceed safe levels), bacterial contamination, chemical residues
Body must absorb nutrients from food
Can break if: Diarrheal diseases (children may have diarrhea 6-12 times annually in poor WASH areas), intestinal parasites, repeated infections
Body must utilize nutrients for growth, immunity, energy
Can break if: Malaria increases metabolic demands, HIV/AIDS increases requirements while reducing absorption, chronic inflammation
Especially critical for infants and young children
Can break if: Poor breastfeeding, inappropriate complementary feeding, inadequate responsive feeding, lack of healthcare seeking
| Country | Anemia Prevalence (%) | Year |
|---|---|---|
| Tanzania | 38.8% | 2023 |
| Kenya | 32.2% | 2023 |
| Uganda | 27.2% | 2023 |
| Rwanda | 16.1% | 2023 |
"New updates of the prevalence of anaemia in women aged 15 to 49 years reveal either no improvement or an increase in prevalence in nearly all regions from 2012 to 2023, and an increase in the global prevalence from 27.6 to 30.7 percent."
Impact: Anemia causes fatigue, impairs cognitive development, increases maternal mortality.
Minimum Dietary Diversity for Women (MDD-W)
SOFI 2025: "The United Nations Statistical Commission officially endorsed the prevalence of minimum dietary diversity (MDD) as a new indicator for monitoring progress towards SDG Target 2.2."
Global Status: Only 65% of women globally achieve minimum dietary diversity. In Uganda's 2019 survey, only 21% of women achieved MDD-W—meaning 79% had inadequate dietary diversity.
Stability refers to the ability to obtain food over time. Even if food intake is adequate today, households are food insecure if they have inadequate access periodically, risking deterioration in nutritional status.
"Since late 2020, domestic food retail prices have risen significantly across most countries. Year-on-year global average food price inflation surged from 5.8 percent in December 2020 to a staggering 23.3 percent in December 2022."
EAC Impact:
⚠️ 24.4% severe food insecurity most acute
✓ Food security peak period
Kenya Drought 2022:
EAC Impact:
Impact on Households:
Ongoing Crisis Since 2013:
Scenario: Father falls ill, cannot work for 3 months
Assets:
Outcome: Used savings, borrowed small amount, mother's income increased. Food security maintained. Father recovered. No lasting impact.
Assets:
Outcome: Sold farming tools, ate only maize/cassava, reduced to 2 meals daily, children withdrawn from school. Slid into food insecurity. Will take years to recover.
Same shock. Vastly different outcomes. Difference? RESILIENCE.
Rwanda's social protection programs (VUP), health insurance (Mutuelle), and promotion of savings groups build household resilience—enabling stability even through shocks.
The four pillars are NOT independent. They're deeply interconnected. A problem in one pillar creates cascading effects in others.
"The complexity of the food security problem can be simplified by focusing on four distinct, but interrelated dimensions: food availability, access, utilization, and stability."
All pillars influence and depend on each other
Kenya Drought 2022 Example:
DRC Infrastructure Example:
The Numbers:
Why? Availability alone cannot ensure food security. The food exists but people cannot access it due to poverty (41.7% below poverty line), poor infrastructure, gender inequality, and market inefficiencies.
Economic access constraints force poor diet quality:
Burundi Example:
SOFI 2025: "One-third of women and two-thirds of children consumed diets that were not sufficiently diverse, putting them at risk of inadequate intake of essential vitamins and minerals."
Poor health reduces income-earning capacity:
Tanzania Malaria Example:
Intergenerational Impact: Child malnutrition impairs cognitive development, limiting future earning potential. SOFI: "Stunted children earn 20% less as adults, perpetuating poverty across generations."
Poor access → Poor diet quality → Malnutrition → Reduced productivity → Lower income → Poorer access → Worsening malnutrition...
Breaking the Cycle: Rwanda's integrated programs (VUP cash transfers + nutrition education + kitchen gardens + community health workers + Girinka) improved both access AND utilization simultaneously, breaking the reinforcing negative cycle.
South Sudan Case Study - Multi-Pillar Collapse:
Conflict (stability shock) →
Result: 30.0% child stunting despite ongoing food aid. All four pillars collapsed simultaneously.
1994: All four pillars collapsed (genocide, infrastructure destroyed, markets ceased, mass displacement, 50% stunting)
Reconstruction Approach (1994-2024):
Results (2000 → 2024):
Lesson: Stability enables all other pillars. Without security and predictability, investments in agriculture, markets, or health don't yield results.
The four pillars don't exist in isolation—they connect directly to global frameworks monitoring food and nutrition security.
| Indicator | Pillar | Framework(s) |
|---|---|---|
| Prevalence of Severe Food Insecurity (FIES) | Access | SDG 2.1.2 |
| Prevalence of Undernourishment (PoU) | Access | SDG 2.1.1, CAADP |
| Food Price Index/Volatility | Stability | CAADP |
| Stunting Prevalence | Utilization | SDG 2.2.1, CAADP, WHO |
| Wasting Prevalence | Utilization | SDG 2.2.2, CAADP, WHO |
| Anemia (children, women) | Utilization | WHO Global Nutrition Targets |
| Access to Safe Water | Utilization (WASH) | SDG 6.1.1, CAADP |
| Access to Sanitation | Utilization (WASH) | SDG 6.2.1, CAADP |
| Minimum Dietary Diversity (MDD) | Utilization | SDG 2.2 (NEW 2025) |
When you monitor the four pillars using EAC Framework indicators, you're simultaneously:
One assessment, multiple purposes.
All focused on UTILIZATION pillar:
This module introduces household food security assessment and two critical measurement tools used throughout the EAC region.
By completing Module 3A, you will be able to:
| Section | Topic | Duration |
|---|---|---|
| 1 | Why Assessment Matters | 8 min |
| 2 | Five Types of FNS Assessments | 12 min |
| 3 | IPC Classification System | 12 min |
| 4 | Food Consumption Score (FCS) | 10 min |
| 5 | Household Dietary Diversity (HDDS) | 8 min |
| 6 | Module Summary & Quiz | 5 min |
Assessment is the foundation of effective food and nutrition security programming. Without proper assessment, programs risk wasting resources, missing the truly vulnerable, and failing to address root causes.
Karamoja, Uganda (2008): Reports of food shortage led to blanket food distribution to 150,000 people ($12 million budget) with NO assessment or targeting criteria.
Results:
Turkana, Kenya (2016): Flash flooding displaced thousands. A 72-hour rapid assessment was conducted before response.
Results:
Without data, decisions are based on assumptions and guesswork. Measurement provides evidence for decision-making.
Different causes require different responses. Wrong diagnosis = Wrong treatment = Wasted resources.
Baseline assessment is essential for impact evaluation. No baseline = No impact evaluation possible.
A district officer reports "severe food shortage" affecting 200,000 people. As the FNS coordinator, what should be your FIRST step?
Different situations require different assessment approaches. Understanding when to use each type is critical for effective programming.
| Type | Timeline | When to Use | Primary Purpose |
|---|---|---|---|
| 1. Emergency/Rapid | 2-7 days | Sudden crisis | Immediate humanitarian needs |
| 2. Baseline | 4-8 weeks | Before starting program | Enable future impact evaluation |
| 3. Comprehensive | 3-6 months | Strategic planning | In-depth chronic FNS analysis |
| 4. Monitoring | Ongoing | Continuous tracking | Early warning, trends |
| 5. Impact Evaluation | Post-program | After program ends | Prove program caused changes |
Context: River overflow displaced thousands after heavy rains
72-Hour Assessment Timeline:
Key Findings:
Response Mobilized (Day 4): Emergency food rations, cash transfers, water purification, therapeutic feeding, emergency shelter - Total: $450,000
Without baseline data, you CANNOT prove your program caused improvements. Donors increasingly REQUIRE baseline for funding.
Program: 5-year integrated nutrition program, $12 million budget
Baseline Survey (Feb 2021):
Key Baseline Findings:
Impact Measurement: Difference-in-Differences analysis will compare treatment vs control changes to isolate program impact.
Large-scale, in-depth assessments used for national policy and strategic planning. Example: Kenya's 2023 national assessment covered all 47 counties with 3,250 households over 6 months ($500,000 budget).
System: Monthly phone surveys of 1,200 households
Early Warning Success (April 2023):
Program: "One Cow Per Poor Family" - Give dairy cow to poor households
Evaluation: 1,200 treatment + 1,200 control HH, 3.5 years post-distribution
Impact Results:
Outcome: Program scaled from 50,000 to 365,000 cows based on proven impact
| If You Need To... | Use This Assessment Type |
|---|---|
| Respond to sudden crisis within days | ➜ Emergency/Rapid Assessment |
| Measure starting point before program | ➜ Baseline Assessment |
| Understand complex chronic FNS for policy | ➜ Comprehensive Assessment |
| Track trends and detect emerging crises | ➜ Monitoring System |
| Prove program caused changes | ➜ Impact Evaluation (with baseline) |
The Integrated Food Security Phase Classification (IPC) is a standardized scale for classifying the severity of acute food insecurity. It provides a common language used globally.
| Phase | Name | Key Characteristics |
|---|---|---|
| 1 | Minimal/None | • Households meet basic food needs • FCS >42 • GAM <5% |
| 2 | Stressed | • Minimally able to meet food needs • FCS 28.5-42 • GAM 5-10% |
| 3 | Crisis | • Food consumption gaps present • FCS <28.5 (Poor) • GAM 10-15% • 20-40% using crisis coping |
| 4 | Emergency | • LARGE food consumption gaps • Very high acute malnutrition • GAM 15-30% • 40-60% crisis coping |
| 5 | Catastrophe/Famine | • Starvation, death evident • GAM >30% • CDR >2/10,000/day |
IPC requires convergence of evidence across FOUR domains. Single indicators are NOT sufficient!
Domain 1: FOOD CONSUMPTION - Are people eating enough quality food?
Domain 2: LIVELIHOODS - What are people doing to survive?
Domain 3: NUTRITION - Are children malnourished?
Domain 4: MORTALITY - Are people dying at above-normal rates?
An area shows: 35% households with poor FCS, 55% using crisis coping, 12% GAM, and CDR of 0.8/10,000/day. What is the most likely IPC phase?
The Food Consumption Score (FCS) is a composite indicator measuring dietary diversity, food frequency, and relative nutritional importance of different food groups consumed by a household over 7 days.
Developed by: World Food Programme (WFP)
Standard recall period: Last 7 days
Result: Numerical score (0-112) classified as Poor, Borderline, or Acceptable
| Food Group | Examples | Weight | Max Days | Max Points |
|---|---|---|---|---|
| Main staples | Maize, rice, wheat, sorghum, pasta, bread, cassava, potatoes | 2 | 7 | 14 |
| Pulses/legumes | Beans, peas, lentils, groundnuts, soybeans | 3 | 7 | 21 |
| Vegetables | Tomatoes, onions, spinach, kale, cabbage, carrots | 1 | 7 | 7 |
| Fruits | Bananas, mangoes, oranges, papaya, watermelon | 1 | 7 | 7 |
| Meat/fish | Beef, goat, chicken, pork, fish (fresh or dried) | 4 | 7 | 28 |
| Milk/dairy | Fresh milk, powdered milk, yogurt, cheese | 4 | 7 | 28 |
| Sugar | Sugar, honey, jam, sodas, sweet biscuits | 0.5 | 7 | 3.5 |
| Oil/fats | Cooking oil, butter, margarine, ghee | 0.5 | 7 | 3.5 |
| Category | FCS Range | Interpretation | Action |
|---|---|---|---|
| Poor | 0 - 28 | Inadequate food consumption | Priority for food assistance |
| Borderline | 28.5 - 42 | Marginally adequate consumption | Monitor, may need assistance |
| Acceptable | >42 | Adequate food consumption | Generally food secure |
Household consumption over past 7 days:
| Food Group | Days | Weight | Points |
|---|---|---|---|
| Main staples (ugali, rice) | 7 | 2 | 14 |
| Pulses (beans) | 4 | 3 | 12 |
| Vegetables (spinach, tomatoes) | 5 | 1 | 5 |
| Fruits (bananas) | 2 | 1 | 2 |
| Meat/fish (dried fish) | 1 | 4 | 4 |
| Milk/dairy | 0 | 4 | 0 |
| Sugar | 3 | 0.5 | 1.5 |
| Oil | 6 | 0.5 | 3 |
| TOTAL FCS | 41.5 | ||
Classification: Borderline (28.5-42)
Interpretation: This household has marginally adequate food consumption. They eat staples daily and some vegetables/pulses, but limited animal proteins. Should be monitored and may benefit from livelihood support or targeted assistance.
The Household Dietary Diversity Score (HDDS) is a simple count of different food groups consumed by a household in the previous 24 hours. It measures dietary quality and household food access.
Developed by: FAO
Recall period: 24 hours (yesterday)
Result: Score of 0-12 food groups
| Aspect | HDDS | FCS |
|---|---|---|
| Recall period | 24 hours (yesterday) | 7 days |
| What it measures | Variety/diversity only | Diversity + frequency + nutritional weights |
| Food groups | 12 groups | 8 groups |
| Scoring | Simple count (0-12) | Weighted sum (0-112) |
| Ease | Very simple, quick | More complex |
| Best for | Quick snapshot, diversity focus | Comprehensive food security, WFP standard |
| # | Food Group | Examples |
|---|---|---|
| 1 | Cereals | Maize, rice, wheat, sorghum, millet, bread, pasta |
| 2 | Roots & tubers | Cassava, sweet potatoes, yams, Irish potatoes |
| 3 | Vegetables | Tomatoes, onions, spinach, kale, cabbage, carrots |
| 4 | Fruits | Bananas, mangoes, oranges, papaya, watermelon |
| 5 | Meat | Beef, goat, chicken, pork, lamb |
| 6 | Eggs | Chicken eggs, duck eggs |
| 7 | Fish & seafood | Fresh fish, dried fish, fried fish, sardines |
| 8 | Pulses/legumes/nuts | Beans, peas, lentils, groundnuts, soybeans, cashews |
| 9 | Milk & dairy | Fresh milk, powdered milk, yogurt, cheese |
| 10 | Oil & fats | Cooking oil, butter, margarine, ghee |
| 11 | Sugar & honey | Sugar, honey, jam |
| 12 | Miscellaneous | Spices, tea, coffee, salt, condiments |
| HDDS Score | Category | Interpretation |
|---|---|---|
| 0-3 | Very Low Diversity | Severe dietary inadequacy, high food insecurity |
| 4-5 | Low Diversity | Poor diet quality, food insecure |
| 6-7 | Medium Diversity | Moderate diet quality, vulnerable |
| 8-12 | High Diversity | Good diet quality, better food security |
Unlike FCS, there is NO universal threshold for HDDS. Some programs use ≥6 as "adequate" but this varies by context. HDDS is best used to:
For classification into poor/borderline/acceptable, use FCS instead.
Question: "Yesterday, did anyone in your household eat from these food groups?"
| Food Group | Consumed? | Score |
|---|---|---|
| 1. Cereals (ugali, rice) | YES ✓ | 1 |
| 2. Roots & tubers | NO ✗ | 0 |
| 3. Vegetables (spinach, tomatoes) | YES ✓ | 1 |
| 4. Fruits | NO ✗ | 0 |
| 5. Meat | NO ✗ | 0 |
| 6. Eggs | NO ✗ | 0 |
| 7. Fish (dried fish) | YES ✓ | 1 |
| 8. Pulses/legumes (beans) | YES ✓ | 1 |
| 9. Milk & dairy | NO ✗ | 0 |
| 10. Oil & fats | YES ✓ | 1 |
| 11. Sugar | YES ✓ | 1 |
| 12. Miscellaneous (salt, tea) | YES ✓ | 1 |
| TOTAL HDDS | 7 | |
Interpretation: HDDS of 7 = Medium diversity. The household consumed 7 out of 12 food groups yesterday, indicating moderate dietary quality. Missing: roots/tubers, fruits, meat, eggs, milk/dairy.
Note: This is the SAME household as the FCS example. Notice how HDDS gives a quick 24-hour snapshot (7/12 groups), while FCS gave a 7-day frequency-weighted score (41.5 = Borderline).
Food Consumption Score (FCS) and Household Dietary Diversity (HDDS) tell us WHAT people are eating. But they don't tell us:
A household can have "acceptable" FCS today but be in SEVERE crisis because they:
FCS alone is insufficient. You MUST assess coping strategies!
By completing Module 3B, you will be able to:
| Tool | What It Measures | Questions | Best For |
|---|---|---|---|
| Coping Strategy Index (CSI) | Behavioral responses to food shortage | ~10-15 coping behaviors | Early warning, trend analysis |
| Household Hunger Scale (HHS) | Direct hunger experience | 6 questions (3 behaviors) | Simple, rapid hunger assessment |
| FIES | Food insecurity experience | 8 questions (anxiety to starvation) | Global comparability, SDG reporting |
| Section | Topic | Duration |
|---|---|---|
| 1 | Understanding Coping Strategies | 10 min |
| 2 | Coping Strategy Index (CSI) | 15 min |
| 3 | Household Hunger Scale (HHS) | 10 min |
| 4 | Food Insecurity Experience Scale (FIES) | 12 min |
| 5 | Comparing and Selecting Tools | 8 min |
| 6 | Module Summary & Quiz | 5 min |
Coping strategies are the behaviors and actions households adopt to manage food shortages and maintain their consumption in the face of shocks or stresses.
They range from minor dietary adjustments to irreversible livelihood destruction.
Households follow a predictable progression as food insecurity worsens. Understanding this ladder is critical for early warning and appropriate response.
Timing: First response to mild food shortage
Impact: Minimal long-term harm, easily reversible
Examples:
Signal: Household struggling but not yet in danger
Timing: When stress strategies are insufficient
Impact: Threatens future productive capacity, difficult to reverse
Examples:
Signal: Household sacrificing future for present survival
Timing: Desperate last resort
Impact: Destroys livelihood, creates long-term poverty, irreversible
Examples:
Signal: Household in catastrophic crisis, livelihood destroyed
The shift from STRESS to CRISIS coping is your WARNING SIGNAL that food insecurity is escalating rapidly. This is when you must act!
STRESS coping: Monitor, prepare, consider early action
CRISIS coping: Intervene NOW before emergency
EMERGENCY coping: Too late, damage already done
Context: Drought developing in northeastern Uganda
Month 1 (May): Monitoring shows 45% of households reducing meals from 3 to 2 per day (STRESS coping)
Month 2 (June): 35% now selling chickens/goats, 28% withdrawing children from school (CRISIS coping)
Result:
Lesson: The shift from stress to crisis coping was the trigger for action, preventing catastrophe.
The specific strategies in each level vary by context. A strategy that is "stress" in one setting may be "crisis" in another.
Example: Selling a goat
Always adapt coping categories to local context through key informant consultation!
Your assessment finds: 15% of households reducing meals, 42% using crisis coping (selling productive assets, children out of school), 8% using emergency coping. What should you do?
The Coping Strategy Index (CSI) is a composite indicator that quantifies household coping behavior by asking about frequency of specific coping strategies over the past 7 days.
Developed by: CARE & WFP
Variants: Reduced CSI (rCSI - 5 questions), Full CSI (10-15 questions)
Output: Numerical score; higher = more severe coping
For each coping strategy, you ask two questions:
CSI Calculation: Sum of (Frequency × Severity Weight) for all strategies
The Reduced CSI uses 5 universal coping strategies found across most contexts:
| Coping Strategy | Severity Weight | Category |
|---|---|---|
| Rely on less preferred, less expensive foods | 1 | Stress |
| Borrow food or rely on help from friends/relatives | 2 | Stress |
| Limit portion sizes at meal times | 1 | Stress |
| Restrict consumption by adults so children can eat | 3 | Stress |
| Reduce number of meals eaten per day | 1 | Stress |
The 5-question rCSI focuses on DIETARY coping (food-related behaviors) that are universal and comparable across contexts. Crisis/emergency coping varies too much by context for standardization.
For crisis/emergency coping, use expanded CSI or livelihood coping module.
Question: "In the past 7 days, how many days did your household..."
| Strategy | Days (0-7) | Weight | Score |
|---|---|---|---|
| Rely on less preferred foods | 6 | 1 | 6 |
| Borrow food from friends/relatives | 2 | 2 | 4 |
| Limit portion sizes | 5 | 1 | 5 |
| Adults eat less so children can eat | 4 | 3 | 12 |
| Reduce number of meals per day | 3 | 1 | 3 |
| TOTAL rCSI | 30 | ||
Interpretation: rCSI of 30 indicates moderate food insecurity with frequent dietary coping. This household is stressed but managing. Monitor for any shift to crisis coping.
| rCSI Range | Classification | Interpretation |
|---|---|---|
| 0-3 | No/Low Coping | Food secure, minimal stress |
| 4-18 | Medium Coping | Moderate food insecurity |
| 19+ | High Coping | Severe food insecurity |
The thresholds above are GENERAL. You MUST establish context-specific thresholds based on:
Example: Pastoralist areas may have higher "normal" rCSI (15-20) than agricultural areas (5-10).
| Month | Mean rCSI | % Change | Signal |
|---|---|---|---|
| Jan (Baseline) | 8.2 | - | Normal (harvest season) |
| Feb | 9.5 | +16% | Normal variation |
| Mar | 12.1 | +48% | ⚠️ WARNING: approaching threshold |
| Apr | 16.8 | +105% | 🚨 ALERT: Threshold exceeded |
Action Taken (April): Assessment confirmed crop failure. Cash transfer program launched for 45,000 households. Crisis averted.
The Household Hunger Scale (HHS) is a simple, rapid tool measuring the direct experience of household hunger through 6 questions covering 3 behavioral domains.
Developed by: FANTA (Food and Nutrition Technical Assistance)
Questions: 6 (occurrence + frequency for 3 behaviors)
Recall period: Past 4 weeks (30 days)
Output: Score 0-6, classified into Little/None, Moderate, or Severe hunger
HHS was created to be:
HHS asks about THREE hunger-related behaviors, with TWO questions each (occurrence + frequency):
Q1a. In the past 4 weeks, was there ever no food to eat of any kind in your house because of lack of resources to get food?
Q1b. How often did this happen in the past 4 weeks?
Q2a. In the past 4 weeks, did you or any household member go to sleep at night hungry because there was not enough food?
Q2b. How often did this happen in the past 4 weeks?
Q3a. In the past 4 weeks, did you or any household member go a whole day and night without eating anything at all because there was not enough food?
Q3b. How often did this happen in the past 4 weeks?
| HHS Score | Category | Interpretation |
|---|---|---|
| 0-1 | Little to No Hunger | Household experiencing little to no hunger |
| 2-3 | Moderate Hunger | Household experiencing moderate hunger |
| 4-6 | Severe Hunger | Household experiencing severe hunger |
| Question | Response | Score |
|---|---|---|
| Q1a. No food in house? | Yes | - |
| Q1b. How often? | Sometimes (3-10 times) | 2 |
| Q2a. Go to sleep hungry? | Yes | - |
| Q2b. How often? | Rarely (1-2 times) | 1 |
| Q3a. Whole day without eating? | No | 0 |
| TOTAL HHS | 3 (Moderate Hunger) | |
| Question | Response | Score |
|---|---|---|
| Q1a. No food in house? | Yes | - |
| Q1b. How often? | Often (>10 times) | 3 |
| Q2a. Go to sleep hungry? | Yes | - |
| Q2b. How often? | Sometimes (3-10 times) | 2 |
| Q3a. Whole day without eating? | Yes | - |
| Q3b. How often? | Rarely (1-2 times) | 1 |
| TOTAL HHS | 6 (Severe Hunger) | |
A household reports: Q1b = Often (3 pts), Q2b = Sometimes (2 pts), Q3a = No (0 pts). What is their HHS classification?
The Food Insecurity Experience Scale (FIES) is an 8-question survey measuring household food insecurity based on direct experiences and behaviors, ranging from anxiety about food to severe hunger.
Developed by: FAO (Food and Agriculture Organization)
Questions: 8 yes/no questions
Recall period: Past 12 months
Used for: SDG 2.1.2 monitoring (global food insecurity prevalence)
| Aspect | HHS | FIES |
|---|---|---|
| Questions | 6 (3 behaviors × 2) | 8 (yes/no) |
| Recall period | 4 weeks (30 days) | 12 months |
| Focus | Direct hunger experience | Food insecurity experience (anxiety → hunger) |
| Severity levels | 3 (Little/Moderate/Severe) | 4 (Food Secure/Mild/Moderate/Severe) |
| Best for | Rapid hunger assessment | Annual monitoring, SDG reporting, global comparison |
FIES questions follow a severity progression from WORRY to STARVATION:
"I am going to read you several statements that people have made about their food situation. For each statement, please tell me if the statement was TRUE for you or anyone in your household in the LAST 12 MONTHS (that is, at any time during the past year)."
| # | Question | Severity |
|---|---|---|
| 1 | You were worried you would not have enough food to eat because of a lack of money or other resources | MILD ↓ |
| 2 | You were unable to eat healthy and nutritious food because of a lack of money or other resources | MILD ↓ |
| 3 | You ate only a few kinds of foods because of a lack of money or other resources | MILD ↓ |
| 4 | You had to skip a meal because there was not enough money or other resources to get food | MODERATE ↓ |
| 5 | You ate less than you thought you should because of a lack of money or other resources | MODERATE ↓ |
| 6 | Your household ran out of food because of a lack of money or other resources | MODERATE ↓ |
| 7 | You were hungry but did not eat because there was not enough money or other resources for food | SEVERE ↓ |
| 8 | You went without eating for a whole day because of a lack of money or other resources | SEVERE ↓ |
| Raw Score | Classification | Description |
|---|---|---|
| 0 | Food Secure | No food insecurity experienced |
| 1-3 | Mild Food Insecurity | Worry about food, compromised diet quality |
| 4-6 | Moderate Food Insecurity | Reduced food intake, skipped meals |
| 7-8 | Severe Food Insecurity | Severe hunger, whole days without food |
For official SDG 2.1.2 reporting to FAO, a complex statistical model (Rasch model) is used to calibrate scores. For program-level use, the simple raw score method above is sufficient.
Responses over past 12 months:
| Question | Response | Score |
|---|---|---|
| 1. Worried about not having enough food | YES | 1 |
| 2. Unable to eat healthy/nutritious food | YES | 1 |
| 3. Ate only a few kinds of foods | YES | 1 |
| 4. Had to skip a meal | YES | 1 |
| 5. Ate less than you should | YES | 1 |
| 6. Household ran out of food | NO | 0 |
| 7. Hungry but did not eat | NO | 0 |
| 8. Went whole day without eating | NO | 0 |
| TOTAL FIES | 5 (Moderate Food Insecurity) | |
Interpretation: This household experienced moderate food insecurity over the past year, including worry, poor diet quality, and reduced food intake, but not severe hunger.
Prevalence of Moderate or Severe Food Insecurity (SDG 2.1.2):
| Country | % Population (Moderate + Severe) | Trend |
|---|---|---|
| Burundi | 72.4% | ↑ Worsening |
| Kenya | 44.1% | → Stable |
| Rwanda | 38.7% | ↓ Improving |
| Tanzania | 51.2% | → Stable |
| Uganda | 47.5% | ↑ Worsening |
Source: FAO State of Food Security and Nutrition in the World 2024
You now have THREE tools measuring coping and hunger. How do you choose which to use?
| Aspect | CSI / rCSI | HHS | FIES |
|---|---|---|---|
| What it measures | Coping behaviors to manage food shortage | Direct hunger experience | Food insecurity experience (worry → starvation) |
| Recall period | 7 days | 4 weeks | 12 months |
| Number of questions | 5 (rCSI) or 10-15 (CSI) | 6 | 8 |
| Time to administer | 3-5 minutes | 2-3 minutes | 3-4 minutes |
| Complexity | Medium (frequency × weight) | Low (simple scoring) | Low (yes/no questions) |
| Sensitivity to change | HIGH (weekly changes) | MEDIUM (monthly changes) | LOW (annual changes) |
| Best for early warning | ✓✓✓ Excellent | ✓✓ Good | ✗ Poor (too slow) |
| Emergency assessment | ✓✓ Good | ✓✓✓ Excellent | ✗ Not suitable |
| Annual monitoring | ✓✓ Good | ✓ Acceptable | ✓✓✓ Excellent |
| Global comparability | ✓ Limited | ✓✓ Good | ✓✓✓ Excellent (SDG) |
Situation: Sudden crisis (flood, drought), need rapid hunger assessment within days
Primary tool: HHS
Why: Simplest, fastest, directly measures hunger, 4-week recall captures recent crisis
Optional add: rCSI if time allows (adds coping behavior context)
Situation: Monthly monitoring to detect emerging food security crisis
Primary tool: rCSI
Why: 7-day recall = highly sensitive to recent changes, tracks coping escalation, trend analysis
Frequency: Monthly surveys to track rCSI trends and trigger alerts
Situation: Government tracking food insecurity for SDG 2.1.2 reporting
Primary tool: FIES
Why: Required for SDG reporting, globally comparable, captures annual experience
Additional tools: Can add FCS, HDDS, HHS for comprehensive picture
Situation: Measuring food security before and after a 3-year program
Recommended combination: FCS + rCSI + HHS
Why:
Together they provide comprehensive food security picture
Situation: Identifying most vulnerable households for food/cash assistance
Primary tool: FCS + Livelihood Coping (crisis/emergency strategies)
Why: FCS classifies current consumption; crisis/emergency coping identifies those destroying livelihoods to maintain consumption
Target: Poor FCS OR crisis/emergency coping (either qualifies for assistance)
Recommended Combinations:
Total survey time for all tools together: ~25-30 minutes per household
Tools Used: FCS + rCSI + HHS + Livelihood Coping
Sample: 850 households surveyed over 10 days
Key Findings:
| Indicator | Result | Insight Provided |
|---|---|---|
| Poor FCS | 38% | Current inadequate consumption |
| rCSI (mean) | 24.5 | High dietary coping, double baseline |
| HHS Severe Hunger | 22% | Direct hunger experienced |
| Crisis Coping | 51% | Majority destroying productive capacity |
| Emergency Coping | 14% | Livelihoods already destroyed |
Critical Insight from Combined Tools:
FCS alone showed 38% with poor consumption. But when crisis/emergency coping was added, total vulnerable population was 65% (38% poor FCS + 27% borderline/acceptable FCS BUT using crisis/emergency coping).
Response Decision:
Outcome: Multi-tool assessment prevented underestimation and ensured adequate response
You need to track food security trends monthly to provide early warning of emerging crisis in a drought-prone area. Which tool is MOST appropriate?
Food security and nutrition are intimately connected but NOT the same:
Food Security: Availability, access, and utilization of food
Nutrition Security: Adequate nutritional status resulting from food intake, care practices, health, and sanitation
You can have:
Situation: Bushenyi District, southwestern Uganda
Food Security Assessment:
Nutrition Survey Reveals:
Root Causes Found:
Lesson: Food security indicators showed "acceptable" situation, but nutrition indicators revealed severe hidden malnutrition. Both assessments are essential!
By completing Module 3C, you will be able to:
| Section | Topic | Duration |
|---|---|---|
| 1 | Anthropometric Indicators (Stunting, Wasting, Underweight) | 12 min |
| 2 | MUAC Measurement & Classification | 10 min |
| 3 | Infant and Young Child Feeding (IYCF) Indicators | 12 min |
| 4 | Survey Design & Sampling Methods | 10 min |
| 5 | Data Quality Assurance | 8 min |
| 6 | Integrating FNS Assessments | 8 min |
| 7 | Module Summary & Quiz | 5 min |
Anthropometry is the measurement of the human body. In nutrition, we measure body size (weight, height, arm circumference) to assess nutritional status, particularly in children under 5 years.
Why children under 5? Most vulnerable to malnutrition, easier to measure, reflects household food security
We combine weight, height, and age to create three key indicators:
What: Child's height compared to expected height for their age
Measures: CHRONIC malnutrition (long-term)
Threshold: < -2 SD = Stunted
Meaning: Past deprivation, irreversible
What: Child's weight compared to expected weight for their height
Measures: ACUTE malnutrition (current)
Threshold: < -2 SD = Wasted (MAM), < -3 SD = Severely wasted (SAM)
Meaning: Recent/current crisis, reversible
What: Child's weight compared to expected weight for their age
Measures: OVERALL malnutrition (composite)
Threshold: < -2 SD = Underweight
Meaning: Can reflect acute or chronic or both
Child is TOO SHORT for their age due to prolonged inadequate nutrition, repeated infections, and poor care practices during the first 1,000 days (pregnancy to 2 years).
Classification: Height-for-age Z-score < -2 SD from WHO reference median
| Z-Score Range | Classification | Prevalence Threshold | Public Health Significance |
|---|---|---|---|
| ≥ -2 SD | Normal | - | - |
| < -2 SD | Stunted | < 20% | Low |
| 20-29% | Medium | ||
| 30-39% | High | ||
| ≥ 40% | Very High | ||
| < -3 SD | Severely Stunted | - | Part of overall stunting |
Key Facts About Stunting:
Child is TOO THIN for their height due to recent rapid weight loss or failure to gain weight, typically from acute food shortage or severe illness.
Classifications:
| GAM Prevalence | Public Health Significance | IPC Acute Malnutrition Phase |
|---|---|---|
| < 5% | Acceptable | Phase 1 (None/Minimal) |
| 5-9.9% | Poor | Phase 2 (Alert) |
| 10-14.9% | Serious | Phase 3 (Serious) |
| ≥ 15% | Critical | Phase 4 (Critical) |
| ≥ 30% | Extremely Critical | Phase 5 (Catastrophic) |
Key Facts About Wasting:
Child is TOO LIGHT for their age. Reflects both acute and chronic malnutrition but cannot distinguish between them.
Classification: Weight-for-age Z-score < -2 SD
| Z-Score Range | Classification |
|---|---|
| ≥ -2 SD | Normal weight |
| -3 SD ≤ WAZ < -2 SD | Moderately Underweight |
| < -3 SD | Severely Underweight |
Why Underweight is Less Used Now:
Pattern 1: High Stunting, Low Wasting
Pattern 2: Low Stunting, High Wasting
Pattern 3: High Stunting, High Wasting
Pattern 4: Low Stunting, Low Wasting
A nutrition survey finds: Stunting 38%, Wasting 15.5%, Underweight 32%. What is the MOST appropriate interpretation?
Mid-Upper Arm Circumference (MUAC) is the measurement of the circumference of the left upper arm at the mid-point between the shoulder and elbow.
Why MUAC? Simple, fast, requires minimal training, strong predictor of mortality risk, used for SAM screening and admission to treatment programs
| Aspect | MUAC | Weight-for-Height |
|---|---|---|
| Equipment | MUAC tape (cheap, ~$0.50) | Scale + height board (expensive, ~$200+) |
| Training needed | Minimal (1-2 hours) | Significant (2-3 days) |
| Time per child | 30 seconds | 3-5 minutes |
| Age range | 6-59 months | 6-59 months (or 65-110 cm height) |
| Best for | Screening, community mobilization, admission to treatment | Nutrition surveys, precise prevalence estimates |
| Mortality prediction | Better predictor | Good predictor |
| Mother can do it | YES (with color-coded tapes) | NO (requires trained staff) |
< 115 mm
Severe Acute Malnutrition (SAM)
Action: Immediate referral to therapeutic feeding program
115 to < 125 mm
Moderate Acute Malnutrition (MAM)
Action: Supplementary feeding program
≥ 125 mm
Well-nourished
Action: Continue good feeding practices
Regardless of MUAC, a child with bilateral pitting edema (swelling in both feet that leaves an impression when pressed) is automatically classified as SAM and requires immediate treatment.
How to check: Press thumb firmly on both feet for 3 seconds. If impression remains for ≥3 seconds on both feet = edema = SAM
| Purpose | MUAC Use | Setting |
|---|---|---|
| Community Screening | Identify SAM/MAM children for treatment admission | Health posts, mobile clinics, community health workers, mass screening campaigns |
| Nutrition Surveys | Estimate prevalence of acute malnutrition in population | SMART surveys, rapid nutrition assessments |
| Discharge Criteria | Determine when child is recovered from SAM/MAM | OTP (Outpatient Therapeutic Program), SFP (Supplementary Feeding Program) |
| Mother MUAC | Mothers measure own children, self-refer to clinic if red/yellow | Community-based management of acute malnutrition (CMAM) |
Studies show only 40-60% overlap between children identified as SAM by MUAC vs WHZ.
MUAC identifies: Younger, shorter, more at risk of death
WHZ identifies: Older, taller children who are thin
WHO Recommendation (2013): Use BOTH criteria. A child meets SAM if EITHER MUAC < 115 mm OR WHZ < -3 OR bilateral edema.
Context: Remote pastoralist area, drought developing
Challenge: No nutrition survey for 18 months (insecurity), no data on malnutrition levels
Solution: Train 120 community health volunteers to do MUAC screening
Results in 2 weeks:
Action Taken:
Outcome: MUAC screening provided rapid data for emergency response decision, identified and referred vulnerable children, all at minimal cost. Full SMART survey confirmed results 4 weeks later.
The first 1,000 days (pregnancy to age 2) are the most critical for nutrition. Poor feeding practices during this window lead to irreversible stunting, illness, and developmental delays.
IYCF indicators measure: Breastfeeding practices and complementary feeding quality
WHO defines 8 core indicators to assess infant and young child feeding practices:
| Indicator | Age Group | What It Measures |
|---|---|---|
| 1. Early initiation of breastfeeding | 0-23 months | % put to breast within 1 hour of birth |
| 2. Exclusive breastfeeding < 6 months | 0-5 months | % receiving only breastmilk (no water/other foods) |
| 3. Continued breastfeeding at 1 year | 12-15 months | % still breastfeeding |
| 4. Continued breastfeeding at 2 years | 20-23 months | % still breastfeeding |
| 5. Introduction of solid/semi-solid foods | 6-8 months | % receiving complementary foods |
| 6. Minimum dietary diversity (MDD) | 6-23 months | % consuming ≥5 of 8 food groups |
| 7. Minimum meal frequency (MMF) | 6-23 months | % fed minimum # times (varies by breastfeeding status) |
| 8. Minimum acceptable diet (MAD) | 6-23 months | % meeting both MDD AND MMF |
Infant receives ONLY breastmilk (including expressed milk) from birth to 6 months. NO water, NO other liquids, NO other foods.
Exceptions allowed: Oral rehydration solution (ORS), vitamins, minerals, medicines
Why Exclusive Breastfeeding Matters:
| EBF Rate | Assessment |
|---|---|
| ≥ 70% | Excellent |
| 50-69% | Good |
| 30-49% | Fair |
| < 30% | Poor |
EAC Regional Data (2020-2023):
Child 6-23 months consumed foods/beverages from at least 5 out of 8 food groups in the previous 24 hours.
The 8 Food Groups for MDD:
Scoring: Consumed ≥5 groups = Meets MDD (Yes = 1, No = 0)
Child Age: 12 months
Foods consumed in past 24 hours:
Result: 5 food groups consumed → MEETS MDD ✓
Child fed the minimum number of times appropriate for their age and breastfeeding status in the previous 24 hours.
| Age | Breastfed | Non-Breastfed |
|---|---|---|
| 6-8 months | ≥ 2 times/day | ≥ 4 times/day |
| 9-23 months | ≥ 3 times/day | ≥ 4 times/day |
Note: Non-breastfed children require more frequent feeding because they don't have the on-demand nutrition from breastmilk.
Composite indicator. Child receives BOTH:
PLUS breastfed children must be breastfeeding, non-breastfed must receive ≥2 milk feeds
Why MAD is Important:
Despite high EBF rates, complementary feeding quality is poor:
This means 75-88% of children 6-23 months are NOT receiving adequate diet! This is a major driver of stunting in the region.
A 10-month-old breastfed child consumed in past 24 hours: breastmilk, ugali (maize), groundnut paste, mango, egg. How many times fed solid foods: 2 times. Does this child meet Minimum Acceptable Diet (MAD)?
You cannot measure every household. You must select a representative SAMPLE that allows you to make valid conclusions about the entire POPULATION.
Bad sampling = Biased results = Wrong decisions = Failed interventions
| Survey Type | Purpose | Typical Sample | Duration |
|---|---|---|---|
| SMART Survey | Standardized nutrition + mortality assessment in emergencies | 300-900 HH | 2-3 weeks |
| Comprehensive Food Security Assessment | In-depth FS analysis (FCS, CSI, HHS, livelihoods) | 1,200-2,400 HH | 4-6 weeks |
| Rapid Assessment | Quick snapshot for emergency decision (HHS, FCS) | 200-400 HH | 3-7 days |
| Demographic & Health Survey (DHS) | National representative survey (health, nutrition, population) | 8,000-15,000 HH | 6-12 months |
| Post-Distribution Monitoring | Verify aid delivery and measure immediate outcomes | 100-300 HH | 1-2 weeks |
Every household has equal probability of selection. Like lottery - put all household names in a hat, randomly draw sample.
Pros: Unbiased, statistically valid
Cons: Requires complete household list (sampling frame), may be geographically scattered (high cost)
Best for: Small, defined populations (e.g., camp, single village)
Select every Kth household from a list.
Example: Population 5,000 HH, sample 500 needed → K = 5,000/500 = 10 → Select every 10th household
Process: Start at random number (1-10), then select every 10th: 7, 17, 27, 37...
Pros: Simple, ensures spread
Cons: Requires ordered list, can be biased if list has patterns
Best for: Sampling from registries, camp lists
Divide population into subgroups (strata), then randomly sample from each stratum.
Example Strata: Livelihood zones, geographic regions, urban/rural, wealth groups
Pros: Ensures representation of all subgroups, allows sub-group analysis
Cons: Requires knowledge of stratum proportions, more complex
Best for: Diverse populations where subgroup comparison needed
Two-stage process:
Example: 30 clusters × 20 households/cluster = 600 households total
Pros: Cost-effective (teams stay in cluster area), feasible without complete HH list
Cons: Less precise than simple random (design effect), requires larger sample
Best for: Large geographic areas, SMART surveys, national surveys
Standard Sample Sizes for Nutrition Surveys:
| Survey Type | Children (6-59 mo) | Households | Clusters |
|---|---|---|---|
| SMART (district level) | 300-450 | 300-450 | 30 × 10-15 HH |
| SMART (with mortality) | 600-900 | 600-900 | 30 × 20-30 HH |
| Regional comparison | 900+ (300 per region) | 900+ | Varies |
Free software developed by SMART for:
Download: Available free at smartmethodology.org
Training: Required for proper use (2-3 day SMART methodology training)
Method 1: Random Walk / Spin the Pen
Method 2: Segmentation
Method 3: Household List (if available)
Always use probability-based random selection!
"Garbage in, garbage out." Poor quality data leads to wrong conclusions, inappropriate responses, and wasted resources.
Quality data requires: Proper training, standardized procedures, supervision, and verification at every step.
Training & Standardization
Field Monitoring
Data Cleaning & Analysis
Verify that enumerators can measure weight and height accurately and consistently before deploying to the field.
Standard Protocol:
Pass Criteria (WHO Standards):
| Measurement | Precision (TEM) | Accuracy vs Expert |
|---|---|---|
| Weight | < 100g | Mean difference < 100g |
| Height | < 7mm | Mean difference < 7mm |
| MUAC | < 2mm | Mean difference < 2mm |
No exceptions! Poor measurement = invalid survey results.
After data collection, run plausibility checks using ENA for SMART software:
| Criteria | Excellent | Good | Acceptable | Problematic |
|---|---|---|---|---|
| Overall data quality score | 0-4 | 5-9 | 10-14 | ≥ 15 |
| % children with WHZ flags | < 2.5% | 2.5-5% | 5-7.5% | > 7.5% |
| % children with HAZ flags | < 2.5% | 2.5-5% | 5-7.5% | > 7.5% |
| Standard deviation of WHZ | < 1.10 | 1.10-1.15 | 1.15-1.20 | > 1.20 |
| Digit preference (weight) | < 7 | 7-12 | 12-20 | > 20 |
| Problem | Likely Cause | Solution |
|---|---|---|
| High WHZ flags (> 7.5%) | Measurement errors in weight/height | Re-train teams, verify equipment calibration, increase supervision |
| SD of WHZ > 1.20 | Inconsistent measurements between teams | Re-standardization, pair weak with strong measurers |
| Digit preference (weight ending in 0 or 5) | Not reading scale precisely | Re-train on scale reading, enforce recording to 0.1 kg |
| Age heaping (many children "24 months") | Mothers don't know exact age, enumerators guessing | Use event calendar, require birth documentation where possible |
| Too many missing values | Incomplete interviews, refusals | Improve community mobilization, revisit households, extend survey if needed |
Situation: SMART survey, 450 children measured over 10 days
Day 3 Evening Check: Supervisor notices Team 2 has WHZ SD = 1.45 (very high, should be < 1.20)
Investigation:
Action Taken:
Outcome:
Food security and nutrition are interconnected. Assessing only one gives incomplete picture and leads to inappropriate responses.
Integrated assessment = Comprehensive understanding = Appropriate, effective response
Integration has TWO meanings:
Benefits:
Typical Combination:
Key Questions:
MODULE 1: Household Roster & Demographics (5 min)
MODULE 2: Food Security (12 min)
MODULE 3: Livelihoods & Shocks (8 min)
MODULE 4: IYCF (if child 0-23 months present) (7 min)
MODULE 5: Anthropometry (if child 6-59 months present) (10 min)
MODULE 6: WASH & Health (5 min)
TOTAL SURVEY TIME: 35-40 minutes per household
Integrated survey findings:
Overall Results:
Cross-Tabulation Analysis Revealed:
| FCS Category | % HH | GAM Rate | Stunting Rate | Mean rCSI |
|---|---|---|---|---|
| Poor (0-28) | 22% | 14.2% | 45% | 32.1 |
| Borderline (28.5-42) | 38% | 8.8% | 36% | 19.3 |
| Acceptable (>42) | 40% | 3.1% | 24% | 7.2 |
Key Insights from Integration:
Program Response Designed:
Without integration: Would have designed separate, potentially conflicting FS and nutrition programs. Integration enabled coherent, layered response addressing both immediate (wasting) and chronic (stunting) needs.
IPC has PARALLEL classifications for food insecurity and malnutrition that should be analyzed together:
| IPC AFI Phase | Typical GAM Range | Typical FCS Poor | Typical rCSI |
|---|---|---|---|
| Phase 1 (Minimal) | < 5% | < 10% | < 8 |
| Phase 2 (Stressed) | 5-9.9% | 10-20% | 8-18 |
| Phase 3 (Crisis) | 10-14.9% | 20-35% | 18-28 |
| Phase 4 (Emergency) | ≥ 15% | > 35% | > 28 |
An integrated survey finds: FCS Poor 12%, GAM 16%, Stunting 38%. What is the BEST interpretation and response?
The East African Community Framework for Assessment of Food and Nutrition Security establishes 23 core indicators organized around the four pillars of food security. This framework provides technical guidance on:
These reports provide baseline data, regional comparisons, and country-specific estimates for EAC Partner States.
Definition: The physical availability of food in sufficient quantities and quality to meet dietary needs.
Influenced by:
Indicators in this pillar: 4-9 (Dietary Energy Supply, Macronutrients, Import Dependency, Self-Sufficiency)
Definition: An individual's ability to obtain food through production, purchasing power, and resources.
Influenced by:
Indicators in this pillar: 1-3 (Severe Food Insecurity, Dietary Energy Consumption, Prevalence of Undernourishment)
Definition: The ability to properly use and absorb food consumed, ensuring adequate nutrient intake.
Influenced by:
Indicators in this pillar: 12-21 (Anaemia, Vitamin A, Stunting, Wasting, Underweight, Low Birth Weight, Adolescent Overweight)
Definition: The resilience of the food system in the face of challenges and disruptions.
Influenced by:
Indicators in this pillar: 10-11 (Food Price Index, Food Price Volatility)
| Assessment Type | Purpose | Timeframe | Key Characteristics |
|---|---|---|---|
| Chronic Food Security | Monitor long-term structural food insecurity | Annual/Multi-year | Uses core indicators; measures persistent conditions |
| Acute Food Security | Identify emergency situations requiring urgent response | Real-time/Seasonal | Uses IPC, CARI; classifies severity of crises |
| Transitory Food Security | Monitor temporary food insecurity from shocks | Quarterly/Seasonal | Uses HEA, early warning indicators |
| Comprehensive Nutrition | Assess nutritional status and determinants | 3-5 years | Anthropometric measurements, biomarkers |
| Pillar | Indicator Numbers | Count | Focus Area |
|---|---|---|---|
| ACCESS | 1, 2, 3 | 3 | Food insecurity experience, energy consumption, undernourishment |
| AVAILABILITY | 4, 5, 6, 7, 8, 9 | 6 | Energy supply, macronutrients, trade dependency |
| STABILITY | 10, 11 | 2 | Food prices and volatility |
| UTILIZATION | 12-21 | 10 | Nutrition outcomes, anaemia, micronutrients, anthropometry |
| WASH (Cross-cutting) | 22, 23 | 2 | Sanitation and safe water |
| Definition | The percentage of the population experiencing severe food insecurity based on the Food Insecurity Experience Scale (FIES), defined as individuals who have run out of food, experienced hunger, and at worst, gone for days without eating |
| Pillar | ACCESS |
| Framework Link | SDG 2.1.2 |
| Unit | Percentage (%) |
| Custodian Agency | FAO, National Statistics Offices |
The FIES is an experience-based metric that asks eight questions about conditions associated with limited food access:
| Year | Severe FI (%) | Severe FI (millions) | Moderate or Severe FI (%) | Moderate or Severe FI (millions) |
|---|---|---|---|---|
| 2019 | 9.1 | 706 | 25.0 | 1,943 |
| 2020 | 10.6 | 851 | 28.8 | 2,308 |
| 2021 | 11.1 | 880 | 29.1 | 2,303 |
| 2022 | 10.8 | 862 | 28.9 | 2,306 |
| 2023 | 10.7 | 864 | 28.9 | 2,326 |
| 2024 | 10.1 | 828 | 28.0 | 2,285 |
Source: FAO, IFAD, UNICEF, WFP & WHO, 2025, Table 2.3, p. 14
| Subregion | Severe FI (%) | Moderate or Severe FI (%) | Population Affected (millions) |
|---|---|---|---|
| Africa Total | 22.2 | 58.9 | 892.7 |
| Eastern Africa | 24.8 | 64.9 | 325.1 |
| Middle Africa | 37.0 | 77.3 | 164.6 |
| Northern Africa | 12.4 | 35.1 | 95.5 |
| Southern Africa | 10.7 | 26.5 | 19.4 |
| Western Africa | 20.2 | 63.2 | 288.1 |
Source: FAO, IFAD, UNICEF, WFP & WHO, 2025, Table 2.3, p. 16
| Country | Severe FI (%) | Moderate or Severe FI (%) | Trend 2022-2024 |
|---|---|---|---|
| Burundi | 20.9 | 70.8 | Stable/Increasing |
| Kenya | 28.1 | 73.9 | Increasing ⚠️ |
| Rwanda | Data not available | Data not available | - |
| Tanzania | 24.4 | 58.0 | Stable |
| Uganda | 24.9 | 74.2 | Increasing ⚠️ |
Source: FAO, IFAD, UNICEF, WFP & WHO, 2025, Table A1.1, pp. 148-162
| Assessment Type | Use of Indicator | Frequency | Complementary Indicators |
|---|---|---|---|
| Chronic FNS Assessment | Primary outcome indicator for long-term food access monitoring | Annual | PoU, HDDS, economic access indicators |
| IPC Acute Analysis | Contextual indicator for understanding chronic conditions underlying acute crisis | As needed during acute assessments | Food consumption scores, livelihood coping |
| CARI Assessment | Can be integrated as one component of food security classification | Quarterly/Seasonal | FCS, rCSI, household expenditure |
| National Surveys (DHS/MICS) | Core module for national food security monitoring | 3-5 years | Anthropometric data, household characteristics |
| Pillar | ACCESS |
| Framework Link | Food Balance Sheets (FBS) |
| Unit | Kilocalories per capita per day (kcal/capita/day) |
| Custodian Agency | FAO |
Calculated from Food Balance Sheets using the formula:
Where:
Based on prevalence of undernourishment data as a proxy for inadequate DEC:
| Country | PoU 2024 (%) | Implication for DEC |
|---|---|---|
| Burundi | Data limited | Likely inadequate DEC based on high food insecurity |
| Kenya | 28.1 | Significant population with inadequate energy intake |
| Tanzania | 23.8 | Large portion of population energy-deficient |
| Uganda | 36.9 | Severe energy deficit affecting over one-third ⚠️ |
| Definition | The percentage of the population whose habitual food consumption is insufficient to provide the dietary energy levels required to maintain a normal, active, and healthy life |
| Pillar | ACCESS |
| Framework Link | CAADP, SDG 2.1.1 |
| Unit | Percentage (%) |
| Custodian Agency | FAO |
The PoU is estimated using a probability distribution model with three parameters:
| Year | PoU (%) | Number Undernourished (millions) |
|---|---|---|
| 2019 | 7.5 | 581 |
| 2020 | 8.5 | 670 |
| 2021 | 8.8 | 698 |
| 2022 | 8.7 | 696 |
| 2023 | 8.5 | 688 |
| 2024 | 8.2 | 673 |
| Year | Africa PoU (%) | Number Undernourished (millions) |
|---|---|---|
| 2015 | 15.9 | 194 |
| 2019 | 17.4 | 233 |
| 2020 | 18.5 | 255 |
| 2021 | 18.9 | 267 |
| 2022 | 18.9 | 273 |
| 2023 | 20.0 | 296 |
| 2024 | 20.2 | 307 |
| Country | 2020 (%) | 2021 (%) | 2022 (%) | 2023 (%) | 2024 (%) | 2024 (millions) |
|---|---|---|---|---|---|---|
| Burundi | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. |
| DRC | 28.3 | 28.2 | 28.7 | 29.7 | 30.2 | 64.3 |
| Kenya | 26.6 | 27.1 | 25.7 | 25.9 | 25.9 | 14.7 |
| Rwanda | 24.4 | 24.4 | 24.4 | 24.4 | 24.4 | 3.4 |
| South Sudan | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. |
| Tanzania | 20.6 | 20.6 | 20.2 | 20.2 | 20.2 | 13.4 |
| Uganda | 21.5 | 21.5 | 22.0 | 22.0 | 36.9 | 17.4 |
| Pillar | AVAILABILITY |
| Framework Link | Food Balance Sheets (FBS) |
| Unit | Kilocalories per capita per day (kcal/capita/day) |
| Custodian Agency | FAO |
Components measured:
| Indicator | Nutrient | Unit | WHO/FAO Recommendation | % of Energy |
|---|---|---|---|---|
| Indicator 5 | Protein | g/capita/day | 0.83 g/kg body weight/day (~50-60g for adults) | 10-15% |
| Indicator 6 | Fats | g/capita/day | ~44-77g for 2,000 kcal diet | 20-35% |
| Indicator 7 | Carbohydrates | g/capita/day | ~275-375g for 2,000 kcal diet | 55-75% |
| Food Group | Africa (% of Healthy Diet Basket) | Asia (% of Healthy Diet Basket) |
|---|---|---|
| Staple Foods | 188% | 108% |
| Animal Source Foods | -33% | 40% |
| Fruits | -40% | -31% |
| Vegetables | -55% | 25% |
Note: Negative percentages indicate insufficient availability to meet healthy diet basket requirements.
Source: FAO, IFAD, UNICEF, WFP & WHO, 2023, Table 8, p. 62
| Assessment Type | Use of Macronutrient Indicators | Complementary Analysis |
|---|---|---|
| Chronic FNS Assessment | Assess dietary quality beyond just energy | Dietary diversity scores, nutrition outcomes |
| Nutrition Strategy Development | Identify nutrient gaps for intervention planning | Cost of nutritious diet, food composition data |
| Agricultural Policy | Guide crop diversification and livestock promotion | Production statistics, market analysis |
| School Feeding Programs | Design nutritionally adequate meals | Menu planning, local food availability |
| Definition | The ratio of food imports to total domestic food supply (imports + domestic production), indicating the extent to which a country relies on imports to meet food needs |
| Pillar | AVAILABILITY |
| Unit | Percentage (%) or Ratio |
| Custodian Agency | FAO, World Bank, National Trade Statistics |
Alternative calculation:
IDR = (Food Imports / Total Food Supply) × 100Where Total Food Supply = Production + Imports - Exports ± Stock Changes
| Country | Overall Food Import Dependency | Key Import-Dependent Commodities |
|---|---|---|
| Burundi | Moderate (30-50%) | Wheat, rice, cooking oils, sugar |
| DRC | Low-Moderate (20-40%) | Wheat, rice, processed foods |
| Kenya | Moderate-High (40-60%) | Wheat, rice, maize (periodic), oils, sugar |
| Rwanda | Moderate-High (40-60%) | Wheat, rice, cooking oils, processed foods |
| South Sudan | High (60-80%) ⚠️ | Cereals, oils, pulses (conflict-affected) |
| Tanzania | Low-Moderate (20-40%) | Wheat, rice, oils |
| Uganda | Moderate (30-50%) | Wheat, oils, sugar |
Note: Classifications based on known trade patterns; specific values require official trade and production statistics
| Definition | The ratio of domestic food production to domestic food consumption, indicating the degree to which a country can meet its food needs from domestic production |
| Pillar | AVAILABILITY |
| Unit | Percentage (%) or Ratio |
| Custodian Agency | FAO |
Where:
| Ratio | Status | Implication |
|---|---|---|
| >100% | Net exporter, surplus production | Food security buffer, export revenues |
| 100% | Self-sufficient | Production matches consumption |
| 80-99% | Minor production deficit | Some import needs, manageable |
| <80% | High dependency on imports | Vulnerable to global market shocks |
"The shock caused by the war [in Ukraine] acted mainly through the global food and agricultural markets, as it involved two major global producers of agricultural commodities... In this context, one of the main impacts of the war has been to increase international prices of food"
These indicators should be tracked annually to:
In this module, we will examine the indicators that measure the stability and utilization dimensions of food security in the East African Community region.
By the end of this module, you will be able to:
Explore how food price index and price volatility affect food security stability in the EAC region.
Comprehensive analysis of nutrition outcome indicators including micronutrient deficiencies and anthropometric measurements.
Practical frameworks for implementing nutrition assessments across the EAC region.
Guidelines for developing appropriate interventions based on indicator findings.
Recall from our conceptual framework that stability relates to the resilience of the food system in the face of challenges and disruptions.
The stability pillar addresses: "Can people maintain access to adequate food over time?"
A population may have sufficient food availability and access today, but without stability, they remain vulnerable to future food insecurity.
The Food Price Index measures changes in international food prices. It is expressed as an index value, typically with a base year set at 100.
| Attribute | Detail |
|---|---|
| Unit of Measurement | Index |
| Custodian Agency | FAO |
| Geographical Coverage | National, Regional, Global |
| Timeliness | One month lag |
The Food Price Index is calculated as a weighted average of price indices for various food commodities:
Rising food prices can signal potential food security crises, particularly for net food-importing countries and poor households who spend a large proportion of their income on food.
Sharp increases in the index can prompt governments to implement safety net programs, subsidies, or strategic grain reserve releases.
Countries can use price trends to make informed decisions about food imports and trade agreements.
For EAC Partner States, monitoring the Food Price Index is particularly important because:
Food Price Volatility measures the degree of variation in food prices over time. It captures the unpredictability and instability of food prices rather than just their level.
| Measurement Method | Description |
|---|---|
| Standard Deviation | Measures absolute variation in prices around the mean |
| Coefficient of Variation (CV) | Standard deviation ÷ mean (expressed as %) Preferred method for comparisons |
| Price Variance | Square of the standard deviation |
The utilization pillar addresses how the body uses the food consumed. Even when food is available and accessible, poor utilization can result in malnutrition.
The indicators in this pillar primarily focus on nutritional outcomes, which are the most direct measures of food utilization. We will examine:
| Indicator # | Indicator Name | Category | Priority |
|---|---|---|---|
| 12 | Anaemia among children 6-59 months | Micronutrient | High |
| 13 | Anaemia among pregnant women | Micronutrient | High |
| 14 | Anaemia among non-pregnant women | Micronutrient | Moderate |
| 15 | Vitamin A deficiency (children 6-71 months) | Micronutrient | High |
| 16 | Vitamin A deficiency (women 15-49 years) | Micronutrient | Moderate |
| 17 | Prevalence of stunting | Anthropometric | High |
| 18 | Prevalence of underweight | Anthropometric | Moderate |
| 19 | Prevalence of wasting | Anthropometric | High |
| 20 | Low Birth Weight | Birth outcome | High |
| 21 | Adolescent overweight | Emerging issue | Moderate |
The prevalence of children aged 6-59 months with a hemoglobin concentration below the age-specific cutoff value.
| Severity | Hemoglobin Level (g/dL) |
|---|---|
| Normal | ≥ 11.0 |
| Mild anemia | 10.0 - 10.9 |
| Moderate anemia | 7.0 - 9.9 |
| Severe anemia | < 7.0 |
| Country | Anemia Prevalence (%) | Public Health Significance |
|---|---|---|
| Burundi | 35-40% | Moderate to Severe |
| DRC | 60-65% | Severe |
| Kenya | 35-38% | Moderate |
| Rwanda | 35-38% | Moderate |
| South Sudan | 55-60% | Severe |
| Tanzania | 55-58% | Severe |
| Uganda | 52-55% | Severe |
Four out of seven EAC countries have SEVERE public health problems with childhood anemia (≥40% prevalence). This requires urgent, comprehensive interventions.
The prevalence of pregnant women with a hemoglobin concentration below 11.0 g/dL.
| Country | Anemia Prevalence (%) |
|---|---|
| Burundi | 28-32% |
| DRC | 45-50% |
| Kenya | 38-42% |
| Rwanda | 18-22% |
| South Sudan | 45-50% |
| Tanzania | 45-48% |
| Uganda | 32-35% |
Reduce anemia in women of reproductive age by 50% by 2025
The prevalence of vitamin A deficiency among children aged 6-71 months, defined as serum retinol concentration below 0.70 μmol/L.
| Country | Vitamin A Deficiency (%) | Public Health Significance |
|---|---|---|
| Burundi | 25-30% | Severe |
| DRC | 60-65% | Severe |
| Kenya | 8-12% | Moderate |
| Rwanda | 12-15% | Moderate |
| South Sudan | 45-50% | Severe |
| Tanzania | 30-35% | Severe |
| Uganda | 18-22% | Moderate |
The percentage of children aged 0-59 months who have height-for-age below minus two standard deviations (-2 SD) from the median of the WHO Child Growth Standards.
| Country | Stunting Prevalence (%) | Severe Stunting (%) | Trend |
|---|---|---|---|
| Burundi | 52-56% | 20-24% | Declining slowly |
| DRC | 42-46% | 15-18% | Stable/Increasing |
| Kenya | 26-28% | 8-10% | Declining |
| Rwanda | 33-36% | 10-12% | Declining |
| South Sudan | 31-35% | 10-13% | Stable |
| Tanzania | 31-34% | 10-12% | Declining slowly |
| Uganda | 28-31% | 9-11% | Declining slowly |
Burundi has one of the highest stunting rates in the world (52-56%). Even the best-performing EAC country (Kenya at 26-28%) still has a HIGH prevalence by WHO standards (>20%).
Stunting reflects chronic undernutrition and indicates:
The first 1,000 days (from pregnancy to 24 months of age) is the critical period for preventing stunting. After age 2, stunting is largely irreversible.
The percentage of children aged 0-59 months who have weight-for-height below minus two standard deviations (-2 SD) from the median of the WHO Child Growth Standards.
| Country | Wasting Prevalence (%) | Severe Wasting (%) | Status |
|---|---|---|---|
| Burundi | 5-6% | 1-2% | Poor |
| DRC | 7-9% | 2-3% | Poor |
| Kenya | 4-5% | 1-2% | Acceptable |
| Rwanda | 2-3% | 0.5-1% | Acceptable |
| South Sudan | 11-16% | 4-6% | Serious to Critical |
| Tanzania | 4-5% | 1-2% | Acceptable |
| Uganda | 4-5% | 1-2% | Acceptable |
Wasting indicates acute malnutrition resulting from:
The proportion of live births with birth weight below 2,500 grams.
| Country | Low Birth Weight (%) | Data Quality |
|---|---|---|
| Burundi | 12-15% | Moderate |
| DRC | 10-14% | Limited |
| Kenya | 8-11% | Good |
| Rwanda | 6-8% | Good |
| South Sudan | 13-18% | Limited |
| Tanzania | 8-10% | Moderate |
| Uganda | 10-12% | Moderate |
The percentage of adolescents aged 10-19 years who have a body mass index (BMI) above the age- and sex-specific 85th percentile of the WHO growth reference.
| Country | Adolescent Overweight (%) | Adolescent Obesity (%) | Trend |
|---|---|---|---|
| Burundi | 3-5% | 1-2% | Stable |
| DRC | 4-6% | 1-2% | Stable |
| Kenya | 8-12% | 3-5% | Increasing (urban) |
| Rwanda | 5-7% | 2-3% | Increasing slightly |
| South Sudan | 3-5% | 1-2% | Limited data |
| Tanzania | 6-9% | 2-4% | Increasing (urban) |
| Uganda | 5-8% | 2-3% | Increasing (urban) |
| Assessment Type | Frequency | Key Indicators | Sample Size | Duration |
|---|---|---|---|---|
| Demographic & Health Survey (DHS) | Every 5 years | All anthropometric (17-21), Anemia (12-14), WASH (22-23) | 8,000-12,000 HH | 6-8 months |
| Multiple Indicator Cluster Survey (MICS) | Every 3-5 years | All anthropometric, Anemia (12-14), WASH (22-23) | 10,000-15,000 HH | 6-8 months |
| National Nutrition Survey | Every 2-3 years | All nutrition indicators (12-21) | 5,000-8,000 HH | 3-4 months |
| SMART Survey | Annual or emergency | Wasting (19), Stunting (17), MUAC, Mortality | 900-1,200 children | 2-3 weeks |
| Sentinel Site Surveillance | Continuous/monthly | Wasting (19), MUAC, admissions | Facility-based | Ongoing |
| Rapid Assessment | As needed (emergency) | Wasting (19), MUAC, Food security | 400-600 HH | 1-2 weeks |
Conduct assessments at consistent times annually for trend comparison. Ideally, conduct both lean season and post-harvest assessments to capture the full spectrum of seasonal variation.
All indicators should be disaggregated by the following dimensions to identify inequalities:
| Dimension | Categories | Purpose |
|---|---|---|
| Geographic | Region, Province, District, Urban/Rural | Identify geographic disparities and hotspots |
| Age | 0-5 months, 6-11 months, 12-23 months, 24-59 months | Target age-appropriate interventions |
| Sex | Male, Female | Identify gender differences in nutrition outcomes |
| Wealth Quintile | Poorest to Richest (5 quintiles) | Address economic inequalities |
| Maternal Education | None, Primary, Secondary, Higher | Target education interventions |
| Livelihood Zone | Pastoral, Agro-pastoral, Agricultural, Urban | Context-specific programming |
| Quality Measure | Anthropometric Surveys | Micronutrient Surveys | Frequency |
|---|---|---|---|
| Standardization exercises | Required before data collection | Required before blood collection | Pre-survey |
| Duplicate measurements | 5-10% of children | 5% of samples | During survey |
| Plausibility checks | Flag outliers, check distributions | Laboratory quality control | During analysis |
| Inter-observer reliability | Test between measurers | Test between technicians | Weekly during survey |
| Equipment calibration | Daily calibration of scales/boards | Daily calibration of HemoCue | Daily |
| Supervisor spot checks | 10% of teams daily | 10% of procedures | Daily |
| Indicator | Analysis Software | Reference Standards | Flagging Criteria |
|---|---|---|---|
| Stunting, Wasting, Underweight | WHO Anthro, ENA for SMART | WHO 2006 Standards | WHO flagging criteria |
| Adolescent BMI | WHO AnthroPlus | WHO 2007 Reference | WHO flagging criteria |
| Anemia | Excel, SPSS, Stata | WHO cutoffs by age/pregnancy status | None (laboratory values) |
| Vitamin A | Excel, SPSS, Stata | WHO cutoffs (< 0.70 μmol/L) | Laboratory QC protocols |
| Stakeholder | Report Type | Timing |
|---|---|---|
| National Government | Preliminary findings | 2 weeks post-survey |
| Full report | 2-3 months post-survey | |
| Policy brief | 3 months post-survey | |
| EAC Secretariat | Standardized country report | 3 months post-survey |
| Regional synthesis | 6 months (annual) | |
| Partners/Donors | Technical report | 2-3 months post-survey |
| Communities | Community feedback | 1 month post-survey |
| Research Community | Scientific publications | 6-12 months post-survey |
This matrix guides program planners in selecting appropriate interventions based on the severity of malnutrition indicated by assessment findings.
| Indicator Finding | Severity | Priority Level |
|---|---|---|
| Stunting > 40% | Very High | National emergency priority |
| Stunting 30-40% | High | Subnational priority |
| Wasting ≥ 15% | Critical Emergency | Immediate humanitarian response |
| Wasting 10-15% | Serious | Humanitarian programming required |
| Anemia > 40% | Severe public health problem | National program required |
| Vitamin A deficiency ≥ 20% | Severe | National supplementation program |
Implementation level: National, with dedicated budget allocation
Timeline: Immediate activation within 48-72 hours
| Indicator Type | Examples | Monitoring Frequency |
|---|---|---|
| Output Indicators (Program reach) |
• Number of children receiving vitamin A • Number of pregnant women receiving IFA • Coverage of CMAM programs • Number of households receiving food assistance |
Monthly |
| Outcome Indicators (Immediate effects) |
• Vitamin A serum retinol levels • Hemoglobin concentrations • Recovery rates from acute malnutrition • Exclusive breastfeeding rates |
Quarterly to Annually |
| Impact Indicators (Long-term effects) |
• Stunting prevalence • Wasting prevalence • Underweight prevalence • Micronutrient deficiency prevalence |
Every 2-5 years (major surveys) |
Establish baseline values for all indicators before program implementation. Conduct midline assessments to adjust programming. Conduct endline assessments to measure impact.
| Skill Area | Current Capacity in EAC | Priority | Training Needed |
|---|---|---|---|
| Anthropometric measurement | Moderate | High | Standardization training, quality assurance |
| Blood sample collection and analysis | Low to Moderate | High | Laboratory techniques, HemoCue use, biosafety |
| Survey design and sampling | Moderate | Medium | Complex survey design, sample size calculation |
| Data management and analysis | Moderate | High | WHO Anthro, ENA for SMART, statistical software |
| Report writing and interpretation | Moderate | Medium | Technical writing, data interpretation |
| MUAC screening | Moderate to High | Medium | Community health worker training refreshers |
| IPC/CARI analysis | Low | High | IPC/CARI technical training certification |
From Fragmentation to Harmonization: Building a Unified Regional FNS Monitoring System
From Fragmentation to Harmonization: Building a Unified Regional FNS Monitoring System
"Imagine you're a regional policymaker at the EAC Secretariat in Arusha. It's 2022, and you need to understand the food security situation across all seven Partner States to prepare for a crucial donor meeting.
You receive Kenya's report using IPC methodology. Tanzania sends a DHS-based nutrition survey. Rwanda provides their VAM data. Uganda shares HEA results. Burundi's data is six months old. DRC uses different indicators entirely. South Sudan's report is delayed due to conflict.
The critical question before you: How many people in the EAC region are food insecure?
You cannot answer. The data isn't comparable. The methodologies differ. The timing is inconsistent. The indicators don't align.
This is the problem the EAC Framework was designed to solve. This is why harmonization matters."
Scenario: You're tasked with preparing a comprehensive regional food security report for a donor coordination meeting where $500 million in funding will be allocated.
The Problem: Seven countries, seven different reporting approaches.
Result: Regional report impossible to produce. Donors frustrated. Resources misallocated. Vulnerable populations invisible.
Result: Comprehensive regional report published November 2025. 34.0% regional stunting (16.2M children) identified. Targeted resource allocation. Evidence-based programming.
Same outcome—hunger. Completely different causes. Different solutions needed.
This is why we need the Four Pillars framework with harmonized measurement. Without it, we're flying blind—treating symptoms without understanding root causes, wasting resources on ineffective interventions.
"You've come a long way in your learning journey. Let's connect the pieces.
Module 1 established the WHY: 90 plus million people in the EAC face food insecurity. We defined food and nutrition security comprehensively and distinguished chronic, transitory, and acute food insecurity—each requiring different assessment approaches.
Module 2 provided the conceptual structure through the Four Pillars: Availability, Access, Utilization, and Stability. You learned how these pillars interconnect, and how failures in one pillar cascade to others.
Module 3 delivered the operational methods—the actual tools you'll use. IPC for emergency classification, FCS and HDDS for food security measurement, CSI and HHS for coping and hunger, anthropometry and IYCF for nutrition assessment. Standardized tools that enable comparable data collection across contexts.
Module 4 presented WHAT to measure: the 23 core indicators covering all four pillars. These common indicators enable regional aggregation and comparison.
But here's the critical question: How do these pieces come together to create a regional monitoring system that actually works? This is what Module 5 addresses—the institutional architecture, coordination mechanisms, data flows, and reporting structures that transform fragmented country assessments into actionable regional intelligence."
Established the WHY
Provided the CONCEPTUAL STRUCTURE
Delivered the OPERATIONAL METHODS
Presented WHAT TO MEASURE
You now have:
How do these pieces come together to create a regional monitoring system that actually works?
This is what Module 5 addresses: the institutional architecture, coordination mechanisms, data flows, and reporting structures that transform fragmented country-level assessments into actionable regional intelligence.
What you'll learn:
What you'll learn:
What you'll learn:
What you'll learn:
What you'll learn:
"Effective food and nutrition security monitoring doesn't happen by accident. It requires clear institutional structures with defined roles and responsibilities at every level—from the household where data is generated, to the regional secretariat where it informs policy for 280 million people.
The EAC Framework establishes a five-tier architecture. Think of it as a pyramid. At the base: households and communities generating primary data. Above that: districts aggregating and quality-checking. Then: national governments coordinating and analyzing. Next: the EAC Secretariat synthesizing regional intelligence. And at the top: global systems like the SDGs and SOFI reports.
Each tier has specific functions. Each depends on the others. And critically—data flows both UP for regional aggregation, and DOWN for local action. Let's explore each tier in detail."
The EAC Framework organizes FNS monitoring across five interconnected levels, each with distinct roles and responsibilities.
Tier 1: Household & Community| Data Type | Examples | Collection Method |
|---|---|---|
| Food Security | FCS, HDDS, rCSI, HHS, FIES | Household questionnaires |
| Child Nutrition | Weight, height, MUAC, edema | Anthropometric measurement |
| IYCF Practices | Breastfeeding, complementary feeding | Maternal interviews |
| WASH | Water source, sanitation facilities | Household observation |
| Livelihoods | Income sources, assets, shocks | Household interviews |
"In Gicumbi District, 450 trained Community Health Workers conduct monthly MUAC screening of all children 6-59 months in their assigned villages (25-30 households each). Data is recorded on mobile phones using RapidSMS platform and transmitted immediately to the district health office. This generates real-time nutrition surveillance data that feeds into both national HMIS and food security early warning systems."
"Mwanza District established a District Food Security & Nutrition Desk in 2023, housed within the District Planning Office. A dedicated officer coordinates monthly data collection from 15 wards using standardized formats aligned with the 23 core indicators. Data is entered into DHIS2 platform and synchronized with the National Food Security Information System. The district produces monthly bulletins shared with all stakeholders, triggering early responses when rCSI increases >30% or GAM exceeds 7%."
| Aspect | Details |
|---|---|
| Location | Ministry of Agriculture or dedicated Food Security Ministry |
| Staffing |
• National FNS Coordinator (senior official, Assistant Director level minimum) • Technical specialists: nutrition, agriculture, statistics, early warning • Data management team: database managers, GIS specialists • Minimum 8-12 permanent staff |
| Mandate |
• National focal point for all FNS monitoring • Coordinate EAC Framework implementation • Manage National Food Security Information System • Lead annual assessment workplan development • Serve as secretariat to National Committees |
Composition:
Meeting Frequency: Quarterly (minimum), extraordinary sessions as needed
Responsibilities:
Composition: 15-25 technical experts from:
Meeting Frequency: Monthly during assessment planning/implementation; bi-monthly otherwise
Responsibilities:
"Kenya established the National Drought Management Authority (NDMA) as the national coordination entity for FNS monitoring. NDMA coordinates monthly Vulnerability Assessment across all 47 counties using standardized indicators aligned with the EAC Framework. Data is managed in the National Drought Early Warning System database. A National Steering Committee chaired by the Ministry of Devolution reviews quarterly reports. Technical analysis is led by a 22-member Technical Committee including KBS (statistics), MOH (nutrition), MOA (production), WFP, FAO, UNICEF, and universities. Kenya produces annual comprehensive FNS reports that feed directly into EAC regional reporting."
Institutional Home: Productive Sectors Department, EAC Secretariat, Arusha
Framework Recommendation (Chapter 4, Section 4.1): "Establish a working desk at the EAC to link up with country coordinators to facilitate joint planning, external capacity building and preparation of regional reports."
Staffing:
Data Flow: EAC submits regional aggregates to global databases, but also ensures Partner States submit national data directly to maintain country-level visibility in global systems.
"Having institutional structures is necessary but not sufficient. What truly enables regional monitoring is HARMONIZATION—ensuring that when Kenya measures stunting, and when Tanzania measures stunting, they're measuring it the SAME way, so the numbers can be validly compared and aggregated.
The EAC Framework standardizes across three critical dimensions. First: WHAT to measure—the 23 core indicators with precise definitions. Second: HOW to measure—the methodologies, questionnaires, sampling protocols, and quality standards. Third: WHEN to measure—synchronized assessment calendars and reporting timelines.
Without this triple harmonization, we're back to the 2022 problem—seven countries, seven different approaches, zero comparability. Let's see how harmonization works in practice."
Dr. Amina Kamara, Regional FNS Analyst (composite of real experiences):
"In 2021, I was tasked with preparing a regional overview of child malnutrition in the EAC. I received data from all countries, but here's what I found:
I couldn't produce a valid regional estimate. The methodologies were too different. That's when I understood: without harmonization, we're flying blind at the regional level."
Three Dimensions of Harmonization"Partner States should adopt the minimum set of 23 core indicators with standardized definitions and metadata."
Every Partner State must measure the same 23 indicators using the same definitions. No variations allowed.
Standardized Definition (Framework Annex 1):
| Numerator | Number of children aged 0-59 months with height-for-age <-2 SD from WHO 2006 median |
| Denominator | Total number of children aged 0-59 months measured |
| Unit | Percentage |
| Reference Standard | WHO Child Growth Standards 2006 (NOT NCHS 1977, NOT local references) |
| Measurement | Recumbent length (0-23 months), standing height (24-59 months) |
| Equipment | Standardized length board/stadiometer, calibrated daily |
| Precision | Recorded to nearest 0.1 cm |
| Quality | Enumerators must pass standardization test (TEM <7mm) before survey |
| Analysis Software | WHO Anthro or ENA for SMART (standardized flagging criteria) |
Important: All 23 indicators have this level of detailed metadata specification in the Framework.
"Harmonize/standardize food and nutrition security assessment survey methodologies, instruments, frequency, timing, and reporting."
| Survey Type | Frequency | Primary Indicators | Sample Size | Requirement |
|---|---|---|---|---|
| DHS | Every 5 years | 12-21 (nutrition, WASH) | 8,000-15,000 HH | All 7 states must conduct |
| National Nutrition Survey | Every 2-3 years | 12-21 | 5,000-8,000 HH | If no recent DHS |
| Food Balance Sheets | Annual | 4-9 (availability) | National aggregate | Submitted to FAO |
| HH Budget/Consumption Survey | Every 3-5 years | 1-3, 8-9 | 10,000-20,000 HH | All states |
| Agricultural Production Survey | Annual | 4, 9 | Representative farm sample | All states |
| Price Monitoring | Monthly | 10-11 | Key markets | All states |
| Methodology | Trigger | Indicators | Frequency |
|---|---|---|---|
| IPC Acute Food Insecurity | Early warning alert OR request | Food consumption, livelihoods, nutrition, mortality | As needed |
| IPC Acute Malnutrition | GAM >10% OR rapid deterioration | Anthropometry, mortality, disease, food security | Alongside IPC AFI |
| SMART Survey | Emergency OR routine annual | 17, 19, mortality | Annual in high-burden areas |
| CARI | Quarterly in vulnerable areas | FCS, rCSI, FES | Quarterly (optional) |
Partner States shall use EAC-harmonized questionnaire modules for all FNS assessments to ensure data comparability.
All Partner States must use the identical FCS module:
Translation Requirement: Questionnaires translated to local languages but back-translated and validated to ensure equivalence. A household answering in Kiswahili in Tanzania should understand the question identically to one answering in Kinyarwanda in Rwanda.
Surveys must be nationally representative with adequate sample sizes for:
For Household Surveys (DHS, Nutrition Surveys, Budget Surveys):
All Partner States would use this same calculation approach, ensuring comparable precision.
Standardized quality assurance at three stages:
BEFORE Data Collection:
DURING Data Collection:
AFTER Data Collection:
Countries must achieve minimum "GOOD" data quality score for data to be accepted into regional aggregates. Problematic data quality triggers investigation and potential exclusion from regional reporting until issues resolved.
"Standardize timing and reporting in the EAC."
Malnutrition (especially wasting) is seasonal. If Kenya surveys in March (lean season) and Rwanda surveys in October (harvest), their wasting rates won't be comparable—they're measuring different seasonal realities.
| Period | Assessment Type | All Partner States | Indicator Focus | Reporting Deadline |
|---|---|---|---|---|
| Jan-Mar | Price Monitoring | Monthly data collection | 10-11 | 15th of following month |
| Apr-Jun | Lean Season SMART (high-burden areas) | Areas with >15% GAM history | 17, 19 | 6 weeks post-survey |
| Jul-Sep | Main Assessments Window | DHS, National Nutrition Surveys, Agricultural Surveys | All indicators | Dec 31 of survey year |
| Oct-Dec | Post-Harvest Period | Food Balance Sheet compilation, HH Budget Surveys | 1-9 | Mar 31 following year |
| Ongoing | Routine Surveillance | HMIS (nutrition), Early Warning (monthly), IPC (as needed) | Continuous monitoring | Monthly/Quarterly |
This period balances:
Data Submission Deadline: March 31 (for data from previous calendar year)
What Partner States Submit:
| Date | Activity |
|---|---|
| Jul-Sep 2024 | Partner States conduct main surveys (DHS, nutrition surveys, etc.) |
| Oct-Dec 2024 | Data cleaning, analysis, validation at national level |
| Dec 31, 2024 | Partner States publish national FNS reports |
| Jan-Feb 2025 | Countries prepare data for EAC submission using standard template |
| March 31, 2025 | DEADLINE: All 7 Partner States submit data to EAC Secretariat |
| Apr-May 2025 | EAC Secretariat: data validation, quality checks, request clarifications |
| June 2025 | Regional aggregation and analysis |
| July 2025 | Draft regional report prepared |
| August 2025 | Draft reviewed by Regional Technical Committee |
| September 2025 | Revisions based on feedback |
| October 2025 | Final report to EAC Sectoral Council for validation |
| November 2025 | Regional report published and launched |
This assessment draws on all modules. Each attempt serves a randomly selected 20 questions from a bank of 60, with a countdown timer. Passing score is 80%.