5 th - 9 th December 2011, Rome

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Food consumption score (FCS)
Explore the questionnaire module
Calculate
Create the FC groups
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Dietary diversity (DD)
Explore the questionnaire module
Calculate
 Validate the indicators
 Present the outputs

Dietary diversity
Food frequency
Household Food
Consumption
The number of individual foods or food groups consumed over a reference period (7 days, 24 hours)
Number of days (in the past week) that a specific food item has been consumed by a household
The consumption patterns (frequency *
diversity) of households over the last seven days

Information:
 Weekly frequency of foods and/or food groups


Sources of foods
Numbers of meals
Indicators:
→ FCS
→
→
→
→
DD– dietary diversity
Food and Food group frequency (0-7)
Average number of meals (children/adults)
Sources of food

The Food Consumption Score is a composite score based on dietary diversity , food frequency and relative nutrition importance of different food groups.

 The data have to be collected according to usual food items consumed that are specific to the country’s context.
 Food items are grouped into food groups that are standard.
 The difference between foods and condiments must be captured during the data collection.

1.
2.
3.
4.
5.
Using standard 7-day food frequency data, group all the food items into specific food groups.
Sum all the consumption frequencies of food items of the same group , and recode the value of each group above 7 as 7.
Multiply the value obtained for each food group by its weight and create new weighted food group scores.
Sum the weighed food group scores, thus creating the food consumption score (FCS).
Using the appropriate thresholds , recode the variable food consumption score, from a continuous variable to a categorical variable, to create the food consumption groups.

FCS = a staple x staple
+ a pulse x pulse
+ a veg x veg
+ a fruit x fruit
+ a animal x animal
+ a sugar x sugar
+ a dairy x dairy
+ a oil x oil
FCS x i a i
Where,
Food consumption score
Frequencies of food consumption = number of days for which each food group was consumed during the past 7 days
(7 days was designated as the maximum value of the sum of the frequencies of the different food items belonging to the same food group)
Weight of each food group

FOOD ITEMS
1
Maize , maize porridge, rice, sorghum, millet pasta, bread and other cereals
2 Cassava, potatoes and sweet potatoes
3 Beans. Peas, groundnuts and cashew nuts
4 Vegetables and leaves
5 Fruits
6 Beef, goat, poultry, pork, eggs and fish
7 Milk yogurt and other diary
8 Sugar and sugar products
9 Oils, fats and butter
10 Condiments
Food groups
Cereals and
Tubers
Pulses
Vegetables
Fruit
Meat and fish
Milk
Sugar
Oil
Condiments
Weight
2
1
4
3
1
4
0.5
0.5
0

 The score as a minimum of 0 and a maximum of 112.
 Can be presented as mean or can be recoded into food consumption groups

Once the FCS is calculated, the thresholds for the FC
Groups (FCG) should be determined based on the frequency of the scores and the knowledge of the consumption behaviour in that country/region.

Threshold
0 – 21
21.5 - 35
>35.5
Profiles
Poor food consumption
Borderline food consumption
Acceptable food consumption
Thresholds with oil and sugar eaten on a daily basis
(~7 days per week)
0-28
28.5 - 42
>42.5

A score of 21 was set as barely minimum, scoring below 21, a household is expected NOT to eat at least staple and vegetables on a daily base and therefore considered to have poor food consumption. Between 21 and 35, households are assessed having borderline food consumption.
The value 21 comes from an expected daily consumption of staple and vegetables.
» frequency * weight, (7 * 2 = 14)+(7 * 1 = 7).
The value 35 comes from an expected daily consumption of staple and vegetables complemented by a frequent (4 day/week) consumption of oil and pulses.
» (staple*weight + vegetables*weight + oil*weight + pulses*weight =
7*2+7*1+4*0.5+4*3=35).

……Even though these thresholds are standardized there is always room for adjustments based on evidence……

1.
2.
3.
Consider the basic/minimum food consumption in the country.
Ex. Laos diet is mainly rice and vegetables, but in some country you can have oil and/or sugar consumed daily
Based on the data information and the knowledge of the country try to define the thresholds for poor and borderline consumption.
The thresholds should be changed based on evidence and should be remain the same if you want to compare
FCS of different surveys.

Examples of different thresholds:
 Sudan
 Two different thresholds were used for North and South Sudan
 Haiti
 26 & 46 were used because the consumption of oil and sugar among the poorest consumption were about 5 days per week.

!!!! We have to be careful that changes from the standard are very well justified and reported otherwise we can be viewed as changing the threshold ‘ to get the numbers we want’ !!!!

There are different scores on based on:
 Level
 Individual (women or children) vs Household score
 Recall
 7 days vs 24 hrs
 Different numbers of food groups ( 7 to 16)

Score
HDDS – household 16 food groups
FAO
IFPRI
IDDS – women or children 16 food groups
DDS
7 food groups
Groups
-
-
6+ : high
4.5-6 : medium
<4.5 : low

1.
2.
3.
Group all the food items into specific food groups if necessary.
For each food group create a new binominal variable that has 1 (yes) if the household/ individual consumed that specific food group or 0 (no) if the food did not consume that food.
Sum all the food groups variables in order to create the dd score. The new variable will have 0 as minimum and as maximum the total number of food groups collected (7 to 16).

DD = ∑ P i
DD
P i
Where, dietary diversity score
1 if the food group was consumed, 0 if it was not consumed

Run verifications of the FCS, FCGs DD DD groups by comparing them to other proxy indicators of food consumption, food access, and food security for example:
 Cash expenditures,
 % expenditures on food,
 food sources,
 CSI,
 wealth index,
 number of meals eaten per day, etc.

Correlations with FCS comparing FCS to other food security proxies
Burundi
Pearson Correlation 0.31 kcal/capita/day
Sig. (2-tailed)
Pearson Correlation
<0.01
-0.27
CSI score
Sig. (2-tailed) <0.01
% total cash expenditures on food
Pearson Correlation -0.11 asset index
Sig. (2-tailed)
Pearson Correlation
Sig. (2-tailed)
<0.01
0.24
<0.01 total cash monthly expenditures (LOG)
Pearson Correlation 0.28
<0.01
CSI score
No. of assets
No. of means (adults)
Total per cap. Cash exp. (LOG)
Sig. (2-tailed)
Malawi
Pearson Correlation
Sig. (2-tailed)
Pearson Correlation
Sig. (2-tailed)
Pearson Correlation
Sig. (2-tailed)
Pearson Correlation
Sig. (2-tailed)
-0.30
<0.01
0.40
<0.01
0.33
<0.01
0.31
<0.01
We use correlation when we analyse 2 scale/continuous variables ex.
 FCS with DD
 FCS with Kcal
 DD with asset index

North
Central
South
FCS
45
38
27
DD
6.7
5.1
4.2
Poor FC
Borderline FC
Good FC
Age household head
36
45
42
 We use compare mean when we analyse a scale/continuous variable with a categorical/ nominal one.
 ex.
 FCS by urban/rural
 FCGs by age household head

Laos FCS
Staple
Sugar
Vegetables
Fruit
Anim protein
Pulses
Oil
Milk
49
42
35
28
21
14
7
-
15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90
FCS
 This graph aids in the interpretation and description of both dietary habits and in determining cut-offs for food consumption groups (FCGs).

consumed (*)
(Days/week)
7.00
6.00
5.00
Staple
Fruit
Anim protein
Oil
Pulses
Sugar
4.00
3.00
2.00
1.00
-
0 10 20
(*) w eighted moving average over 7 point range
30 40 50 60
Food Consumption Score
70 80
Vegetables
Milk
90 100
This graph shows the consumption frequency of different food groups by FCS independently and not stacked as the previous graph.

4.
5.
1.
2.
3.
6.
Truncate the FCS variable
Run a frequency of the FCS
Run a compare mean of the FCS and all the food groups included in the FCS
Export frequency and compare mean in excel
Calculate an average of the surrounding values for each food group (to smooth the graph).
Use the ‘area’ or the ‘line’ graph in excel.

100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
1 2 3 4 quintiles de indice de richesse
5
acceptable limite pouvre

acceptable limite pauvre
0 7
Maize
Other Cereals
Beans, Peas
Fruits
Fish
Milk/Yoghurt
Sugar, Honey, Jam
14 21 28 35 42
Rice
Casssava, Sweet Pots, Bananas
Vegetables
Meats
Eggs
Oils/Fat/Butter
49

Poor and Borderline FCG
35%
30%
25%
20%
81
71
81 80 82 77
83 86
78 80 81
84
77
69
77
83
91 89
81
15%
10%
5%
0%
D ah
S uk
N in ul ay aw a m an iy ah
Ta m ee m
E rb il
D ia la
A nb
B ar ag hd ad
B ab il
K ar ba la
W
S as si t al ah
A l D in poor borderline
N aj af
Q ad is si a
M ut ha na
Th i –
Q ar
M is sa n
B as ra h
To ta l
Mean
30
20
10
0
100
90
80
70
60
50
40

food consumption score
CSI wealth index per capita total expenditure per capita non foof expenditure total_Income
Spearman's rho
Correlation Coefficient
Sig. (2-tailed)
N
Correlation Coefficient
Sig. (2-tailed)
N
Correlation Coefficient
Sig. (2-tailed)
N
Correlation Coefficient
Sig. (2-tailed)
N
Correlation Coefficient
Sig. (2-tailed)
N
Correlation Coefficient
Sig. (2-tailed)
N
food consumption score
1
.
24975
-.111(**)
0
8877
.378(**)
0
24972
.406(**)
0
24971
.343(**)
0
24971
.430(**)
0
24934

We have information about source of single food but we need an indication of sources of all the food items consumed in the households.
This indicator can be used as proxy of food access.
( ex. dependency on market, food assistance or own production)



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Transform the single sources (x variables as the food items) into n variables as the different sources of food;
Own production, purchase, food assistance, borrow, exchange, gathering, social network, etc.

Doing this we will have the percentage of food consumed coming from different sources
Ex % coming from purchase and % from food aid etc.
In this computation the sources of food should be weighted on the frequency of the food items consumed.

1.
Copy the food frequency value into new variable called as the different sources.
IF (source_rice =1) ownproduction_rice =consumption_rice.
IF (source_rice =2) purchase_rice = consumption_rice.
IF (source_rice =3) foodaid_rice = consumption_rice .
IF (source_rice =4) gathering_rice = consumption_rice.
IF (source_rice =5) borrowrice = consumption_rice . execute.
Do this computation for all the food items and all the sources.

2.
Add all the variables of different foods with the same sources together in order to create the unique variable of the specific source
COMPUTE ownproduction = ownproduction_rice + ownproduction_tubers + ownproduction_eggs + ownproduction_vegetable + ownproduction_meat + ownproduction_fruit + ……
3.
COMPUTE the total sources of food totsource = ownproduction + fishing + purchase + traded + borrow + exc_labor + exc_item + gift + food_aid +other.

4.
Calculate the % of each food source
COMPUTE pownprod = (ownproduction / totsource)*100.
COMPUTE pfishing = (fishing / totsource)*100.
COMPUTE ppurchase = (purchase / totsource)*100.
COMPUTE pborrow = (borrow / totsource)*100.
COMPUTE pexclabor = (exc_labor / totsource)*100.
COMPUTE pexcitem = (exc_item / totsource)*100.
COMPUTE pfoodaid = (food_aid / totsource)*100.
COMPUTE pother = (other / totsource)*100.

Sources of PDS food basket
100%
80%
60%
40%
20%
64
40
33
47
39
62
52
41
67
54
63
48
66
70
60
58
49
16
0%
D ah uk av a
N in
Su la ym an iy ah
Ta m ee m ppds_pds
Er bi l
D ia la
An ba r
Ba gh da d ppds_purchase
Ba bi l
Ka rb al a
W as si t
Sa la h
A l D in
N aj af
Q ad is si a
M ut ha na
Th i –
Q ar ppds_ownproduction ppds_family
M is sa n
Ba sr ah
OTHER
To ta l
49
Sources of all foods
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
30
19
16
22
0%
D ah uk aw a
N in
Su la ym an iy ah
Ta m ee m
Er bi l
17
D ia la
8
28
An ba r
Ba gh da d
21
15
29
24
Ba bi l
Ka rb al a
W as si t la h
A
Sa l D in
28
21
32 34
26
24
N aj af
Q ad is si a
M ut ha na
Th i –
Q ar
M is sa n
Ba sr ah
17
To ta l
21 p_pds p_purchase p_ow nproduction p_family other

Food sources - rural model
Plains
Coastal
Tonle Sap
Total
Plateau
0% 20% 40% 60% type of source
80%
% own producion % fishing and hunting
% purchased+traded % other
100%
Food sources - urban model
Phnom Penh
Coastal
Total
Plains
Tonle Sap
Plateau
0% 20% 40% 60% type of source
80% 100%
% own producion % fishing and hunting
% purchased+traded % other