Overview
The Food Consumption Score (FCS) is an index that was developed by the World Food Programme (WFP) in 1996. The FCS aggregates household-level data on the diversity and frequency of food groups consumed over the previous seven days, which is then weighted according to the relative nutritional value of the consumed food groups. For instance, food groups containing nutritionally dense foods, such as animal products, are given greater weight than those containing less nutritionally dense foods, such as tubers. Based on this score, a household's food consumption can be further classified into one of three categories: poor, borderline, or acceptable. The food consumption score is a proxy indicator of household caloric availability. Validation studies have demonstrated that the FCS and the Household Dietary Diversity Score (HDDS) are both associated with caloric intake, as well as with each other (Coates et al., 2007; Weismann et al., 2009). While the FCS has been validated against quantity of caloric intake (Leroy et al., 2015), there have been limited studies assessing how well FCS predicts nutrient adequacy (e.g. Marivoet et al 2019), and the results were mixed regarding its performance for this purpose.
Method of Construction
A brief questionnaire is used to ask respondents about the frequency of their household's consumption of eight different food groups over the previous seven days. To calculate the FCS from these results, the consumption frequencies are summed and multiplied by the standardized food group weight (see the food groups and corresponding weights below). Households can then be further classified as having "poor," "borderline," or "acceptable" food consumption by applying the WFP's recommended cut-offs to the food consumption score.
| Food Group | Weight |
| Main staples | 2 |
| Pulses | 3 |
| Vegetables | 1 |
| Fruit | 1 |
| Meat/Fish | 4 |
| Milk | 4 |
| Sugar | 0.5 |
| Oil | 0.5 |
Steps:
- Group food items in the specified food groups (condiments not included)
- Sum all the consumption frequencies of food items within the same group
- Multiply the value of each food group by its weight (see table)
- Sum the weighted food group scores to obtain FCS
- Determine the household's food consumption status based on the following thresholds: 0-21: Poor; 21.5-35: Borderline; >35: Acceptable.
For more in-depth information on calculation of FCS, see the technical document provided by the WFP (2008).
Uses
This indicator is useful for categorizing and tracking households' food security across time, specifically as a proxy for the quantity dimension (i.e. household caloric sufficiency) of food security, for which this indicator has been validated. The FCS captures information about usual household diet, since it asks respondents to recall what they consumed over the past seven days. The FCS can be used in a range of ways, including for program monitoring and evaluation and population-level targeting. Since it is a standardized measure, it can also be useful in comparing households in different locations, as well as tracking cyclical changes in household diet if collected repeatedly across seasons or years. The WFP uses the FCS as part of its Comprehensive Food Security & Vulnerability Analysis (CFSVA) tool to assess food security and vulnerability in crisis-prone populations.
The FCS and HDDS are highly correlated and can be used interchangeably as a measure of household-level diet diversity and as a validated proxy for energy sufficiency in most contexts (Maxwell et al., 2014; Vaitla et al., 2017). There have been limited studies assessing how well FCS predicts nutrient adequacy (e.g. Marivoet et al 2019), and the results were mixed regarding its performance for this purpose. Since the FCS and HDDS provide very similar information, the selection of one over the other can often be driven by the need for comparability with other surveys or by institutional preference. In other words, if an organization or individual is interested in comparing their results to those of a WFP survey, it makes sense to collect the FCS, while a comparison with other surveys may be more appropriately based on the HDDS, if the HDDS had been used previously.
Strengths and Weaknesses
The FCS indicator captures information about usual household diet, as it incorporates consumption frequency over a seven-day period. This is different from the HDDS, which only gathers information about the previous day of consumption (Kennedy et al., 2010). Both the FCS and the HDDS were designed as potentially useful indicators to capture quantity (energy) and quality (nutrient adequacy). There have been limited studies assessing how well FCS predicts nutrient adequacy (e.g. Marivoet et al 2019), and the results were mixed regarding its performance for this purpose.
By applying standard nutritional value weights to the food groups in the index, the WFP intends for the score to be a more accurate reflection of the calorie content of the diet pattern than an index where all food groups are equally weighted. That said, validation research by Weismann et al. (2009) suggests that these weights do not usefully increase the association of the FCS index with caloric intake over an un-weighted version of the index, and the weights themselves are not based on a clearly defined nutritional metric.
The FCS and HDDS need to undergo some adaptation to the context in which they will be used in order for enumerators to be able to list contextually appropriate examples of foods that belong to the food groups in the questionnaire. For both the FCS and HDDS, one challenge is how to capture, and whether to exclude, small amounts of food consumed as seasonings or condiments. For both indicators, research has shown that the ability to accurately predict caloric adequacy is greatly increased by ensuring items consumed in small amounts are excluded so as not to overstate the nutritionally relevant diversity of a household's diet (Lonvon & Mathiassen, 2014).
Additionally, as household-level measures, neither the FCS nor HDDS are sensitive to intra- household inequities in food consumption, and therefore should not be used for interventions specifically targeting individuals, such as nutritionally vulnerable women or children. Please see the Minimum Dietary Diversity for Women (MDD-W) and Minimum Dietary Diversity (MDD) for children 6-23 months indicators for alternative individual-level measures.
Data Source
In order to construct this indicator, household data must be obtained using the WFP's standard food consumption score questionnaire (see page 16). In some cases it may be possible to use secondary data from a seven-day food frequency questionnaire or the consumption module of a Household Consumption and Expenditure Survey (HCES) provided that: 1) the recall is seven days, 2) the frequency of consumption is collected, and 3) the food items can be mapped to the WFP's standard eight food groups (see table above). Additionally, WFP standardized food group weights must be used. More details can be found in the technical guidelines from the WFP (2008) and FCS data for select countries can be found on the Vulnerability Analysis and Mapping Databank and on the VAM Resource Center.
Links to Guidelines
Links to Case Studies
No case study to show for this indicator.
Links to Validation Studies
- Baumann et al., (2013). Validity of food consumption indicators in the Lao context: Moving toward cross-cultural standardization. Food and Nutrition Bulletin.
- Leroy et al., (2015). Measuring the food access dimension of food security: A critical review and mapping of indicators. Food and Nutrition Bulletin.
- • Wiesmann et al., (2009). "Validation of the World Food Programme’s food consumption score and alternative indicators of household food security."
- Maxwell et al., (2014). How do indicators of household food insecurity measure up? An empirical comparison from Ethiopia. Food policy.
- Vaitla et al., (2017). The measurement of household food security: Correlation and latent variable analysis of alternative indicators in a large multi-country dataset. Food Policy.
Links to Illustrative Analyses
- Tiwari et al., (2013) "Shorter, cheaper, quicker, better: Linking measures of household food security to nutritional outcomes in Bangladesh, Nepal, Pakistan, Uganda, and Tanzania."
- WFP, (2016). "Comprehensive food security and vulnerability analysis (CFSVA): The Gambia."
- World Food Program. Jordan Comprehensive Food Security and Vulnerability Assessment (2018).
Download Indicator as PDF
Food Security Dimensions
- Quantity
Data Collection Levels
- Household
Data Sources and Methods
- World Food Programme (WFP) Vulnerability Analysis and Mapping (VAM)
- Food Frequency Questionnaire (FFQ)
- Household Consumption and Expenditure Surveys (HCES)
- Dietary Diversity
Requires Food Composition Database
- No
Please cite as: Data4Diets: Building Blocks for Diet-related Food Security Analysis, Version 2.0. (2023). Tufts University, Boston, MA. https://inddex.nutrition.tufts.edu/data4diets. Accessed on 3 June 2024.
World Food Programme (WFP) Vulnerability Analysis and Mapping (VAM)
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Summary
The Vulnerability Analysis and Mapping (VAM) platform is a central source of food security monitoring data and analysis managed by the World Food Programme (WFP). The platform offers multiple products that allow users to visualize and download data on commodity prices and calculated food security indicators, such as the Food Consumption Score (FCS). In addition, users can access timely geospatial, economic, and food security situational analyses produced by VAM analysts that can offer additional context and insight into a country’s current food security situation.
Two WFP VAM products that are particularly useful for calculating indicators included in the Data4Diets platform include the Economic Explorer and the mVAM Databank. The Economic Explorer, a tool included in the VAM Data Visualization Platform, allows users to visualize and download commodity price data at the country and market levels over time (month and year). The mVAM Databank provides the FCS for select countries, using data collected via mobile technology.
Strengths:
- Contains up-to-date, open data supplemented by dynamic visualizations that allow users to perform preliminary analysis within the platform and download charts as .png files
- Provides monthly and annual data on commodity prices at country and market level
- Multiple types of data and analytic reports provide detailed food security and economic context within individual countries, administrative districts, and markets
Weaknesses:
- The market data available across commodities, dates, or level of collection is not consistent between countries, which limits inter-country comparability
- mVAM Databank with the FCS is only available for a select number of countries, and not all countries included have multiple years of data available
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Food Frequency Questionnaire (FFQ)
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Summary
Food Frequency Questionnaires (FFQ) are a type of dietary assessment instrument that attempts to capture an individual’s usual food consumption by querying the frequency at which the respondent consumed food items based on a predefined food list. Given that food lists are culturally specific, FFQs need to be adapted and validated for use in different contexts (Thompson & Subar, 2013).
FFQs are a common method for measuring dietary patterns in large epidemiological studies of diet and health. FFQs are often limited to the food items that are a source of nutrients related to the particular dietary exposures under study, for example, fruit and vegetable consumption or foods with high levels of saturated fat. Dietary diversity scores are a type of metric that are often calculated from a simplified FFQ (see the description of Dietary Diversity metrics to learn more). Food consumption modules of Household Consumption and Expenditure Surveys (HCES) that use a food list and an extended recall period can also be considered a type of FFQ.
In general, FFQs rely on a longer recall period in order to capture foods that are not consumed every day but are still part of the individual’s typical diet. FFQ recall periods vary greatly, but typically range from 7 to 30 days (although some are as long as one year). A drawback is that recall bias may increase with longer periods of recall (Coates et al., 2012). However, these measures of ‘usual intake’ are a more valid indicator of the relationship between diet and health outcomes than those capturing only a single 24-hour snapshot of the diet (24-hour Dietary Recalls can only provide information on usual intake if data are collected from respondents on multiple non-consecutive days). Longer FFQs can better assess total diets, but shorter FFQs have higher response rates and lower respondent burdens (Thompson & Subar, 2013).
FFQs typically collect information on the frequency of consumption but not necessarily on the quantity consumed. When FFQs do include questions about quantity consumed it is typically based on standard portion sizes, rather than direct weight or use of household utensils. Therefore, FFQs are not as accurate as other quantitative dietary assessment methods (e.g. 24-hour Dietary Recall) (Coates et al., 2012). Additional measurement error is introduced when food lists are not specific to the studied population, when questionnaires use inconsistent or imprecise portion sizes (Shim et al., 2014), or when the food lists are not granular enough to make an accurate match to a food composition table for deriving nutrient content of the diet. Because food lists are developed with a specific population in mind, it can be difficult to accurately compare results across populations (cultures or countries) with different dietary patterns.
Strengths:
- Better at estimating ‘usual diet’ due to longer recall period than the 24-hour Dietary Recall or 24-hour Weighed Food Records
- Captures individual-level dietary patterns
- FFQs can be easier and less time-consuming to implement than a 24-hour Dietary Recall, if the food list is relatively short (e.g. <100 items)
Weaknesses:
- FFQs require substantial up-front investment to develop and validate the instrument (food list and quantities) for a given context or country.
- Usual frequency of intake is prone to measurement error, particularly with recall periods longer than seven days (and usual portion size questions are prone to measurement error)
- If the FFQ is too long it can be more time consuming to administer than a standard 24-hour Dietary Recall and cause respondent fatigue
- Like most surveys, to capture seasonal variation data collection must span the entire year or be repeated in multiple seasons
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Household Consumption and Expenditure Surveys (HCES)
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Summary
Household Consumption and Expenditure Surveys (HCES)—also referred to by a variety of other names including Household Income and Expenditure Surveys (HIES), Household Budget Surveys (HBS), or Living Standards Measurement Surveys (LSMS)—are complex surveys conducted on a nationally representative sample to characterize important aspects of household socioeconomic conditions (Coates et al., 2012). Typically, HCES are conducted every 3-5 years in a range of countries and cover 7,000 to 20,000 households to provide a statistically representative sample (Fiedler et al., 2012). Most HCES are implemented by national statistical agencies, often with technical assistance from the World Bank’s Living Standard Measurement Study (LSMS) group.
The results of HCES have wide-ranging utility. Their primary purpose is to provide information for poverty monitoring, the calculation of national accounts, and as an input for consumer price indices (Smith et al., 2014). However, there is increasing interest in using the food consumption module from HCES as a source of nationally representative data for assessing food security and nutrition. Furthermore, HCES collect a wide range of data on determinants and outcomes (e.g. socioeconomic status, education), potentially enriching food security and nutrition analyses. Based on existing research there is wide consensus that HCES, with carefully designed consumption modules, are a valuable source of data for household-level food security and nutrition measurement (Russell et al., 2018; Zezza et al., 2017).
One of the major drawbacks of using HCES is that the consumption modules are heterogeneous across countries, which means that not all HCES data lend themselves to the same food security and nutrition analyses, and comparisons across countries can be inaccurate. Some of the key ways in which the consumption modules differ across surveys include: 1) the length of the recall period; 2) whether data are collected for acquisition, consumption, or both; 3) whether there is information on the mode of food acquisition (purchases, own production, and in-kind); 4) whether or not information on food consumed away from home is collected and in what form; 5) whether food detail is collected through open recall or a list, and, if a list, how disaggregated and specific the foods and food groups are; and 6) the use of non-standard units without available conversions (Smith et al., 2014). For example, if the food consumption module has a short food list with aggregated items making it difficult to match with a food composition database, excludes food away from home, and has a long recall period (>14 days) then the consumption module may not be adequate for measuring certain food security and nutrition indicators, such as total household-level calorie availability.
While the ‘C’ in HCES stands for ‘consumption’, HCES collect data on acquisition, consumption, or both. While consumption data refers to the food consumed by the household, acquisition data refers to the food acquired through purchases, own-production, and in-kind. Acquisition data serve as a proxy for food consumption, as households may build food stocks or consume food stocks during the reference period, as compared to consumption, which collect data on food consumed in a specified period. This is an important point because some foods (e.g. grains) are not perishable and can be stored, therefore some households may be drawing down stocks acquired to meet current consumption, while other households may be accumulating stocks that will be consumed after that period (Smith et al., 2014). Another type of HCES collects a combination of acquisition and consumption data, wherein households report what they acquired through purchases and what they consumed from own-production and transfers (Smith, 2003). Food consumption estimates generated from acquisition data or a combination of both acquisition and consumption data are typically referred to as 'apparent consumption' in the literature to distinguish from actual consumption (Fiedler & Mwangi, 2016).
The World Bank Microdata Library has the most comprehensive and publicly accessible repository of HCES data. Data also can be accessed—often for a fee—from countries’ National Statistics Office, though each country has its own policies and procedures regarding data sharing. The International Household Survey Network (IHSN) is an informal network to promote data standards and dissemination where additional information (e.g. survey catalogs, guidelines, and software) on existing HCES can be found (IHSN, 2018).
Strengths:
- Typically nationally representative and sometimes representative at provincial and district levels
- Typically collected every 3-5 years, allowing for an examination of trends
- Food consumption data from HCES are an important source of information on food security and nutrition
- Include a wide range of data on determinants and outcomes (e.g. socioeconomic status, education), enabling various analytical options.
Weaknesses:
- Due to issues with the structure of some consumption modules (e.g. no information on food consumed away from home), the data may not be useful for certain food security and nutrition analyses
- Some HCES only measure 'apparent consumption' (based on acquisition data), not actual consumption
- The food list is not always designed with the level of detail needed to make exact matches between the food items in the food list and a food composition database
- Recall periods in HCES vary from 1 to 365 days, with long recall periods (>2 weeks) raising concern about reliability and recall bias
- Household-level data from HCES do not allow for measurement of individual-level food security and nutrition indicators
- Many HCES do not capture seasonal variation
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Dietary Diversity
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Summary
Variation in an individual's diet is associated with the intake of adequate energy and essential nutrients; increasing variety in one’s diet is recommended in most dietary guidelines globally (Ruel, 2003). Dietary diversity is especially important among populations with diets based on starchy staples where micronutrient deficiency is more likely (Ruel, 2003). The most common method of measuring dietary diversity for a household or individual consists of assessing the variety of different food groups consumed in a specific recall period; information on the quantity of foods consumed is not gathered. Indicators of dietary diversity are considered to be useful as measures of impact for programs designed to address nutrition through agricultural pathways.
Dietary diversity can be measured at either the household or the individual level and higher scores represent a more diverse diet. For households, a higher score is an indicator of increased economic access to a varied diet for household members (though the indicator does not reflect intra-household dietary patterns). Household dietary diversity has been shown to be associated with caloric and protein adequacy and household income (Swindale & Bilinksy, 2006).
Individual dietary diversity indices, specifically the Minimum Dietary Diversity for Women (MDD-W) and the Minimum Dietary Diversity (MDD) for children 6-23 months, have been shown to be a rough proxy for diet quality and nutrient adequacy (FAO & FHI, 2016; Moursi et al., 2008). While there is consensus around the significance of dietary diversity, there are multiple approaches to measurement with varied food groups and recall periods (Table 1).
Table 1
Dietary Diversity Score | Data collection level | Number of food groups | Recall period | Purpose | Universal cut-off? | Validated as a proxy measure of… | Promoted by |
Household level measures | |||||||
Household dietary diversity score (HDDS) | Household | 12 | 24 hours | Household dietary diversity and proxy for household food access and socioeconomic status | No | Socioeconomic Status | FAO |
Food consumption score (FCS) | Household | 8 | 7 days | Measures "usual" household consumption; Standardized food group weights are used to construct index. | Yes | Not Validated | WFP |
Individual level measures | |||||||
Minimum Acceptable Diet (MAD) | Infant/child (6-23 months) | 8 (from MDD) | 24 hours | Measures both minimum dietary diversity and minimum meal frequency. | Yes | Not Validated | WHO |
Minimum Dietary Diversity (MDD) | Infant/child (6-23 months) | 8 | 24 hours | Measures infant and child dietary quality and adoption of complementary feeding practices | Yes | Nutrient Adequacy | WHO |
Minimum Dietary Diversity for Women (MDD-W) | (Individual) Women 15-49* | 10 | 24 hours | Dichotomous indicator that measures the dietary diversity of an individual woman; associated with nutrient adequacy in many contexts and can be used as a proxy for overall diet quality | Yes | Nutrient Adequacy | WHO, USAID |
Women’s Dietary Diversity Score (WDDS/IDDS)** | (Individual) Women 15-49* | 9 | 24 hours | Continuous indicator that was the basis for the MDD-W (sometimes referred to as the Individual Dietary Diversity Score (IDDS)) | No | Superseded by MDD-W | |
*Women of reproductive age
**The WDDS, also referred to as the Individual Dietary Diversity Score (IDDS), was not included in the Data4Diets Platform as it has been superseded by MDD-W
Dietary diversity indicators can be constructed using a specific module relevant to that dietary diversity indicator (e.g. Household Dietary Diversity module, MDD module for children from 6-23 months). In addition, the various dietary diversity scores can be constructed from existing data, as long as the recall period is aligned. Some potential data sources include Household Consumption and Expenditure Surveys (HCES), , or Food Frequency Questionnaires. More generally, dietary diversity modules are frequently included as short modules in multi-purpose household survey questionnaires.
Dietary diversity scores are not direct measures of consumption and not all have been validated as proxy measures of nutrient adequacy. A significant drawback of the household-level indicators is that scores do not provide information on whether the household dietary diversity is shared equally by all individual members of the household. For more precise population measures of nutrient adequacy by age/sex groups individual-level data from 24-hour Dietary Recalls or Weighed Food Records should be used.
Strengths
- Relatively easy to use and to integrate as a short module into surveys
- Requires fewer resources than attempting to measure quantitative dietary consumption data for nutrient adequacy
- Calculating the scores is a straightforward process and training others to collect data does not require a large amount of time
- Dietary diversity scores can give an idea of what types of foods are consumed
Weaknesses
- Modules usually require tailoring to specific contexts
- Scores do not provide detailed information on quantitative dietary intakes and are not a direct measure of nutrient adequacy; the cut-offs for the MDD-W do not predict nutrient adequacy in all contexts for all population groups
- Household-level dietary diversity scores do not provide information on individual household members and cannot be used to draw conclusions about individuals
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