Diet Quality Indices for Research in Low and MiddleIncome Countries (LMICs)

Understanding the nutritional status of populations in low and middleincome countries (LMICs) requires reliable, comparable measures of diet quality. Unlike highincome settings, LMICs often confront limited food availability, rapid urbanisation, and a double burden of under and overnutrition. Researchers therefore need diet quality indices that are both culturally relevant and feasible to implement with the resources at hand.

Why Use Diet Quality Indices?

Singlenutrient or foodgroup analyses can miss broader patterns that influence health. Diet quality indices address this by summarising overall dietary adequacy, diversity, and balance into a single score. This offers several advantages for LMIC research:

• Comparability: Scores can be compared across regions, time points and studies.
• Policy relevance: Indexes often align with national dietary guidelines, helping translate findings into action.
• Resource efficiency: Many indices rely on short foodfrequency questionnaires (FFQs) or 24hour recalls, reducing respondent burden.

Key Diet Quality Indices Used in LMIC Settings

Index	Core Components	Data Requirements	Strengths in LMICs
Dietary Diversity Score (DDS)	Number of food groups consumed over a reference period (usually 24h).	Simple 24hour recall or 7day FFQ.	Easy to collect; correlates with micronutrient adequacy.
Minimum Dietary Diversity for Women (MDDW)	9 food groups; threshold = 5 groups.	24hour recall for women of reproductive age.	WHOendorsed; directly linked to micronutrient adequacy.
Minimum Dietary Diversity for Children (MDDC)	8 food groups; threshold = 5 groups.	24hour recall for children 623months.	Used in large nutrition surveys (e.g., DHS, MICS).
Food Consumption Score (FCS)	Weighted frequency of 7 food groups over 7 days.	Householdlevel 7day recall.	Captures both quantity and quality; widely used by WFP.
Healthy Eating Index (HEI2015) Adapted	12 components (e.g., fruits, vegetables, whole grains, added sugars).	Detailed 24hour recall or FFQ.	Provides a nuanced picture of diet conformity with guidelines.
Alternative Healthy Eating Index (AHEI2020) Adapted	11 components focusing on chronicdisease risk factors.	24hour recall or FFQ with portion size data.	Predictive of noncommunicable disease outcomes.
ProVegetarian Diet Index (PDI)	Positive scores for plant foods, negative for animal foods.	FFQ with frequency data.	Useful where plantbased transitions are policy goals.

Adapting Indices to Local Context

Indices originally designed for highincome countries often need modification for LMICs:

1. Foodgroup definitions: Replace whole grains with locally consumed cereals (e.g., millets, sorghum).
2. Portion size estimation: Use culturally appropriate household measures (spoons, cups, local baskets).
3. Seasonality: Adjust scoring windows to capture seasonal availability of fruits and vegetables.
4. Data collection mode: Mobilephonebased surveys can reduce cost and improve reach in remote areas.

Methodological Considerations

1. Choice of Reference Period

Short recall periods (24h) reduce recall bias but may not reflect usual intake. Combining oneday recalls with a foodfrequency module can improve habitual diet estimation.

2. Validation

Whenever possible, compare index scores against a goldstandard method such as multipleday weighed food records or biomarkers (e.g., serum ferritin, vitamin A). Validation studies in LMICs have shown moderate correlations (r0.30.5) between DDS and micronutrient adequacy.

3. Scoring Thresholds

Binary cutoffs (e.g., MDDW 5 groups) simplify analysis but may mask gradations in diet quality. Researchers can create quartilebased categories to explore doseresponse relationships.

4. Household vs. Individual Data

Indices like the FCS capture household food access, while DDS and MDDW focus on individual consumption. Selecting the correct level of analysis depends on the research question (e.g., food security vs. maternal nutrition).

Applications in LMIC Research

Nutrition Surveillance

National surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS) now routinely incorporate MDDW and MDDC, providing comparable data across continents.

Intervention Evaluation

Programmes promoting kitchen gardens, foodfortification, or social protection can use diet quality indices as primary outcomes to assess impact on nutritional adequacy.

Linking Diet Quality to Health Outcomes

Several cohort studies in South Asia and SubSaharan Africa have linked higher DDS or HEI scores with reduced odds of stunting, anemia, and cardiometabolic risk factors.

Policy Development

Indices can inform foodbased dietary guidelines (FBDGs). For example, Ghanas FBDG used findings from MDDW analyses to set targets for fruit and vegetable consumption.

Challenges and Future Directions

• Data quality: Inconsistent training of enumerators can lead to misclassification of food groups.
• Cultural relevance: Some indices overlook traditional foods that are nutritionally valuable but not captured by standard foodgroup lists.
• Integration with foodsystem data: Combining diet quality scores with market price or production data could illuminate affordability barriers.
• Technology adoption: Mobile foodlogging apps and imagebased portion estimation hold promise but require infrastructure and literacy.

Investing in contextspecific validation studies, improving data collection tools, and fostering interdisciplinary collaborations will enhance the utility of diet quality indices for addressing the nutrition transition in LMICs.

Key Takeaways

1. Diet quality indices synthesize complex dietary information into actionable scores.
2. Simple indices (DDS, MDDW/C, FCS) are most feasible for largescale surveys in LMICs.
3. More detailed indices (HEI, AHEI) can be used for focused research on chronic disease risk.
4. Adaptation to local food cultures, validation, and clear documentation are essential for reliable results.
5. These tools support surveillance, program evaluation, and policy formation aimed at improving nutrition across diverse LMIC settings.

For further reading, consult the WHOs guidelines on measuring dietary diversity and the FAOs Food Consumption Score technical documentation.