The Food Consumption Score (FCS) is an index developed by the World Food Programme (WFP) in 1996. The FCS is a household level indicator that aggregates food group diversity and frequency over the past 7 days. These food groups are then weighted according to their relative nutritional value. This means that food groups that are nutritionally-dense such as animal products are given greater weight than those containing less nutritionally dense foods such as tubers. The weights are then added up to come up with a household score which are then used to classify households into either poor, borderline, or acceptable food consumption. The FCS is a measure of quantity of caloric intake.
FCS questionnaire
A brief questionnaire is used to ask respondents about the frequency of their households’ consumption of eight different food groups over the previous seven days. The eight food groups are:
- Main staples
- Pulses
- Vegetables
- Fruit
- Meat/fish
- Milk
- Sugar
- Oil
Following is a model questionnaire that can be used for collecting data on FCS.
Question: I would like to ask you about all the different foods that your household members have eaten in the last 7 days. Could you please tell me how many days in the past week your household has eaten the following foods?
(for each food, ask what the primary source of each food item eaten that week was, as well as the second main source of food, if any)
| Food item | Number of days eaten in the past | Primary source+ | Secondary source+ | |
|---|---|---|---|---|
| 1 | Maize | |||
| 2 | Rice | |||
| 3 | Bread/wheat | |||
| 4 | Tubers | |||
| 5 | Groundnuts and pulses | |||
| 6 | Fish (eaten as a main food) | |||
| 7 | Fish powder (used for flavour only) | |||
| 8 | Red meat (sheep/goat/beef) | |||
| 9 | White meat (poultry) | |||
| 10 | Vegetable oil, fats | |||
| 11 | Eggs | |||
| 12 | Milk and dairy products (main food) | |||
| 13 | Milk in tea in small amounts | |||
| 14 | Vegetables (including leaves) | |||
| 15 | Fruits | |||
| 16 | Sweets, sugar |
| +Food source codes: | |
|---|---|
| Purchase = 1; Own production = 2; Traded goods/services, barter = 3; | Borrowed = 4; Received as gifts = 5; Food aid = 6; Others: (specify) = 7 | | |
This model questionnaire should be adapted to each survey context in which it is to be used.
Calculating the FCS
The FCS or the frequency weighted diet diversity score is a score calculated using the frequency of consumption of different food groups consumed by a household during the 7 days before the survey. Following are the steps to calculate the FCS:
Step 1. Using the standard questionnaire above, group all the food items into specific food groups (see groups in table below).
| Food items | Food groups | Weight | |
|---|---|---|---|
| 1 | Maize , maize porridge, rice, sorghum, millet pasta, bread and other cereals Cassava, potatoes and sweet potatoes, other tubers, plantains | Main staples | 2 |
| 2 | Beans, peas, groundnuts and cashew nuts | Pulses | 3 |
| 3 | Vegetables, leaves | Vegetables | 1 |
| 4 | Fruits | Fruits | 1 |
| 5 | Beef, goat, poultry, pork, eggs and fish | Meat and fish | 4 |
| 6 | Milk yoghurt and other diary | Milk | 4 |
| 7 | Sugar and sugar products, honey | Sugar | 0.5 |
| 8 | Oils, fats and butter | Oil | 0.5 |
| 9 | Spices, tea, coffee, salt, fish powder, small amounts of milk for tea. | Condiments | 0 |
Step 2. Sum all the consumption frequencies of food items of the same group, and recode the value of each group above 7 as 7.
Step 3. Multiply the value obtained for each food group by its weight (see food group weights in table below) and create new weighted food group scores.
Step 4. Sum the weighed food group scores, thus creating the food consumption score (FCS).
Step 5. Using the appropriate thresholds (see below), recode the variable food consumption score, from a continuous variable to a categorical variable.
| FCS | Profiles |
|---|---|
| 0 - 21 | Poor |
| 21.5 - 35 | Borderline |
| > 35 | Acceptable |
Calculating the FCS using dietry
Steps 1, 2, 3, and 4 described above are implemented by the
dietry package using the fcs_recode() and the
fcs_calculate() functions. The fcs_recode()
function is called within fcs_calculate() to perform Step 1
and 2 and then fcs_calculate() completes the operation by
implementing steps 3 and 4. Using the fcs01 dataset, we
apply the fcs_calculate() function to implement these first
3 steps:
## Create a variable map to match food group labels to variables in fcs01 ----
var_map <- fcs_fg_map_variables(
staples = "FCSStap",
pulses = "FCSPulse",
vegetables = "FCSVeg",
fruits = "FCSFruit",
meatfish = "FCSPr",
milk = "FCSDairy",
sugar = "FCSSugar",
oil = "FCSFat",
condiment = "FCSCond"
)
## Calculate scores ----
fcs_calculate(df = fcs01, var_map = var_map, add = TRUE)This provides the following output:
#> FCSStap FCSPulse FCSVeg FCSFruit FCSPr FCSDairy FCSSugar FCSFat FCSCond fcs
#> 1 7 4 5 3 2 1 6 0 2 49.0
#> 2 1 2 2 3 1 5 4 2 6 40.0
#> 3 7 4 4 2 2 1 2 0 7 45.0
#> 4 2 5 4 7 7 0 5 0 0 60.5
#> 5 1 1 1 3 0 4 7 5 0 31.0
#> 6 2 1 2 3 5 4 4 7 0 53.5
#> 7 3 0 4 6 2 2 0 0 2 32.0
#> 8 6 4 1 2 3 2 0 4 0 49.0
#> 9 5 0 1 2 0 4 3 6 4 33.5
#> 10 3 6 1 2 1 5 4 4 0 55.0
#> 11 4 0 4 5 7 3 6 1 5 60.5
#> 12 0 0 7 7 5 5 7 5 1 60.0
#> 13 1 3 4 6 7 6 1 7 1 77.0
#> 14 0 3 5 3 0 4 4 5 4 37.5
#> 15 4 5 5 7 5 3 7 0 2 70.5
#> 16 0 5 4 6 1 6 1 1 5 54.0
#> 17 3 6 6 7 7 3 6 0 1 80.0
#> 18 0 6 3 1 3 0 3 6 2 38.5
#> 19 0 3 3 5 3 0 5 7 1 35.0
#> 20 4 5 2 7 5 3 4 7 7 69.5
#> 21 3 6 6 3 5 3 6 0 4 68.0
#> 22 3 6 5 4 1 7 6 5 0 70.5
#> 23 1 2 3 1 3 6 2 3 1 50.5
#> 24 6 2 3 1 5 1 6 7 5 52.5
#> 25 0 0 0 0 0 0 0 0 0 0.0
#> 26 1 1 1 1 1 1 1 1 1 16.0The output shows the original data.frame but with recoded food groups
consumption frequency and with a calculated food consumption score
(fcs variable).
Finally, to classify the food consumption scores into respective FCS
classifications using the fcs_classify() function as
follows:
## Create a variable map to match food group labels to variables in fcs01 ----
var_map <- fcs_fg_map_variables(
staples = "FCSStap",
pulses = "FCSPulse",
vegetables = "FCSVeg",
fruits = "FCSFruit",
meatfish = "FCSPr",
milk = "FCSDairy",
sugar = "FCSSugar",
oil = "FCSFat",
condiment = "FCSCond"
)
## Calculate scores ----
fcs_df <- fcs_calculate(df = fcs01, var_map = var_map, add = TRUE)
## Classify scores ----
fcs_classify(fcs = fcs_df$fcs, add = TRUE)which gives the following output:
#> fcs fcs_class
#> 1 49.0 acceptable
#> 2 40.0 acceptable
#> 3 45.0 acceptable
#> 4 60.5 acceptable
#> 5 31.0 borderline
#> 6 53.5 acceptable
#> 7 32.0 borderline
#> 8 49.0 acceptable
#> 9 33.5 borderline
#> 10 55.0 acceptable
#> 11 60.5 acceptable
#> 12 60.0 acceptable
#> 13 77.0 acceptable
#> 14 37.5 acceptable
#> 15 70.5 acceptable
#> 16 54.0 acceptable
#> 17 80.0 acceptable
#> 18 38.5 acceptable
#> 19 35.0 borderline
#> 20 69.5 acceptable
#> 21 68.0 acceptable
#> 22 70.5 acceptable
#> 23 50.5 acceptable
#> 24 52.5 acceptable
#> 25 0.0 <NA>
#> 26 16.0 poorA new data.frame is returned with the fcs variable and
the fcs_class variable which indicates whether the
household has a poor, borderline, or
acceptable food consumption score.