Understanding Dietary Diversity Scores (DDS)
Dietary diversity score (DDS) is a qualitative measure of food consumption used as a proxy for nutrient adequacy. It is often used in public health to assess the dietary quality of a population, particularly in settings where more quantitative data collection is difficult or expensive. The score is based on the number of different food groups consumed over a specific recall period, typically the preceding 24 hours. Depending on the unit of assessment, the score can be calculated at the individual or household level. An individual dietary diversity score (IDDS) measures the intake of a specific person, while a household dietary diversity score (HDDS) reflects the economic access to food for the entire household. A higher DDS indicates greater variety, suggesting a more nutrient-rich diet, while a lower score points towards a more monotonous diet.
Step 1: Prepare for Data Collection
Accurate calculation begins with careful preparation. Choosing the correct tool and method is critical for obtaining reliable data that reflects your study's objective.
Choose Your Target Group
Decide whether you will be collecting data at the household or individual level. The choice depends on your research question. If you are interested in overall household food access, use the HDDS. If the focus is on the nutritional intake of vulnerable individuals (e.g., women of reproductive age or young children), use IDDS.
Use a Standardized Questionnaire
Employ a standardized questionnaire, such as those provided by the Food and Agriculture Organization (FAO) or the Food and Nutrition Technical Assistance Project (FANTA). These tools categorize foods into a specific, validated number of food groups. For example, the HDDS uses 12 food groups, while the Minimum Dietary Diversity for Women (MDD-W) uses 10.
Set the Recall Period
Determine the recall period for data collection, with 24 hours being the most common timeframe. Be sure to avoid unusual days, such as religious festivals or celebrations, which might not reflect typical eating patterns. The reference period must be consistent across all interviews.
Train Your Enumerators
Thoroughly train all data collectors on how to administer the questionnaire, probe for all meals and snacks, and properly categorize foods into the predefined food groups. Training is essential to minimize interviewer and recall bias.
Step 2: Conduct Data Collection
Collect data by interviewing a representative sample of your target group. For households, interview the person primarily responsible for food preparation. For individuals, interview the individual themselves or their caregiver. During the interview, use an open recall method to list all foods and drinks consumed over the previous 24 hours. List all ingredients for composite dishes and ensure all snacks are included. After the full list is created, categorize each item into the appropriate food group.
Step 3: Calculate Individual or Household Scores
For each participant (individual or household), a score is calculated by summing the number of unique food groups consumed during the recall period.
Calculation Steps:
- Create a binary variable for each food group. Assign a value of 1 if any food from that group was consumed, and 0 if not.
- Sum the binary variables to get the total DDS for that participant.
Example: Food Groups for HDDS (12 groups)
- Cereals
- Roots and Tubers
- Pulses
- Vegetables
- Fruits
- Meat, Poultry, Offal
- Eggs
- Fish and Seafood
- Milk and Dairy Products
- Oils and Fats
- Sugar/Honey
- Miscellaneous
Step 4: Calculate the Mean Dietary Diversity Score
After collecting the individual or household scores for all participants in your sample, you can compute the mean DDS. The process is a straightforward arithmetic calculation.
Formula for Mean DDS: $$ \text{Mean DDS} = \frac{\sum_{i=1}^{n} \text{DDS}_i}{n} $$
Where:
- $\sum_{i=1}^{n} \text{DDS}_i$ is the sum of the DDS for all participants in the sample.
- $n$ is the total number of participants (individuals or households) surveyed.
For example, if you surveyed 50 households and the sum of all their HDDS is 300, the mean HDDS would be $300 / 50 = 6.0$.
Interpreting the Mean DDS
A higher mean DDS suggests a more diversified diet across the population, which is correlated with better nutritional outcomes. However, interpreting the score requires nuance. Mean DDS is a population-level measure and does not provide information about dietary diversity within households. Additionally, DDS does not account for the quantity of food consumed; it only measures the number of food groups.
For more advanced interpretation, researchers may establish a target based on the average DDS of the wealthier 33% of the surveyed population. Contextual factors like seasonality can also influence the score and should be considered during analysis.
Comparison of Different Dietary Diversity Scores
| Score Type | Unit of Assessment | Food Groups | Primary Purpose |
|---|---|---|---|
| Household DDS (HDDS) | Household | Typically 12 | Proxy for economic access to food; household food security |
| Individual DDS (IDDS) | Individual | Varies by target group (e.g., children) | Proxy for nutrient adequacy; individual diet quality |
| Minimum DDS for Women (MDD-W) | Individual (Women 15-49) | 10 | Dichotomous indicator for micronutrient adequacy in women |
Example Scenario: Calculating Mean HDDS
Imagine you surveyed ten households to assess their dietary diversity using the standard 12 food group HDDS tool. Here is how you would calculate the mean HDDS:
- Survey Data: The collected scores for each household are: 7, 5, 8, 9, 6, 7, 5, 10, 6, 8.
- Sum of Scores: Add all the individual scores together: $7+5+8+9+6+7+5+10+6+8 = 71$.
- Total Households: The number of households surveyed is 10.
- Calculate Mean HDDS: Divide the sum of scores by the total number of households: $71 / 10 = 7.1$.
In this example, the mean HDDS for the surveyed population is 7.1, indicating that, on average, households consumed from just over seven of the 12 food groups in the past 24 hours.
Conclusion: The Importance of Mean DDS in Research
Calculating the mean dietary diversity score is a valuable, low-cost method for assessing nutritional quality at a population level. It serves as a practical indicator for monitoring food security, designing public health interventions, and evaluating the impact of programs on dietary quality. While it has limitations, primarily its qualitative nature and inability to measure intra-household distribution, its simplicity and strong correlation with nutrient adequacy make it a powerful tool for public health practitioners and researchers worldwide. For further reading, consult the FAO guidelines on measuring dietary diversity.
