food consumption analysis 5 th - 9 th december 2011, rome
TRANSCRIPT
Food consumption analysis
5th - 9th December 2011, Rome
Contents Food consumption score (FCS)
Explore the questionnaire module Calculate Create the FC groups
Dietary diversity (DD) Explore the questionnaire module Calculate
Validate the indicators Present the outputs
Definitions
Dietary diversity The number of individual foods or food groups consumed over a reference period (7 days, 24 hours)
Food frequency Number of days (in the past week) that a specific food item has been consumed by a household
Household Food Consumption
The consumption patterns (frequency * diversity) of households over the last seven days
The FOOD CONSUMPTION SCORE (FCS)
Food consumption module
Food consumption module continuedInformation: 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
Food consumption score - FCS
The Food Consumption Score is a composite score based on dietary diversity, food frequency and relative nutrition importance of different food groups.
Data collection
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.
Calculation steps1. Using standard 7-day food frequency data, group all the
food items into specific food groups.2. Sum all the consumption frequencies of food items of the
same group, and recode the value of each group above 7 as 7.
3. Multiply the value obtained for each food group by its weight and create new weighted food group scores.
4. Sum the weighed food group scores, thus creating the food consumption score (FCS).
5. 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
FCS = astaplexstaple+ apulsexpulse+ avegxveg+ afruitxfruit
+ aanimalxanimal+ asugarxsugar + adairyxdairy+ aoilxoil
Where, FCS Food consumption score
xi 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)
ai Weight of each food group
Food groups and weights FOOD ITEMS Food groups Weight
1Maize , maize porridge, rice, sorghum, millet pasta, bread and other cereals Cereals and
Tubers2
2 Cassava, potatoes and sweet potatoes
3 Beans. Peas, groundnuts and cashew nuts Pulses 3
4 Vegetables and leaves Vegetables 1
5 Fruits Fruit 1
6 Beef, goat, poultry, pork, eggs and fish Meat and fish 4
7 Milk yogurt and other diary Milk 4
8 Sugar and sugar products Sugar 0.5
9 Oils, fats and butter Oil 0.5
10 Condiments Condiments 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
FCS thresholds
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.
The typical thresholds are:Threshold Profiles Thresholds with oil
and sugar eaten on a daily basis (~7 days per week)
0 – 21 Poor food consumption
0-28
21.5 - 35 Borderline food consumption
28.5 - 42
>35.5 Acceptable food consumption
>42.5
Why 21 and 35?
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……
How to adapt the thresholds
1. 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
2. Based on the data information and the knowledge of the country try to define the thresholds for poor and borderline consumption.
3. The thresholds should be changed based on evidence and should be remain the same if you want to compare FCS of different surveys.
Example 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’ !!!!
DIETARY DIVERSITY analysis (DD)
Dietary Diversity definition
The number of individual foods or food groups consumed over a reference period (7 days, 24 hours).
Dietary Diversity ScoreThere 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)
Different DD scores
Score Groups
FAO
HDDS – household 16 food groups-
IDDS – women or children 16 food groups
-
IFPRI DDS 7 food groups6+ : high4.5-6 : medium<4.5 : low
Calculation steps 1. Group all the food items into specific food groups if
necessary. 2. 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.
3. 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).
Dietary Diversity Score
DD = ∑ Pi
Where, DD dietary diversity score
Pi 1 if the food group was consumed, 0 if it was not consumed
Validation of the indicators
Validation of the FCS
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 Correlations with FCS comparing FCS to other food security
proxies
Burundi
kcal/capita/day Pearson Correlation 0.31
Sig. (2-tailed) <0.01
CSI score Pearson Correlation -0.27
Sig. (2-tailed) <0.01
% total cash expenditures on food
Pearson Correlation -0.11
Sig. (2-tailed) <0.01
asset index Pearson Correlation 0.24
Sig. (2-tailed) <0.01
total cash monthly expenditures (LOG)
Pearson Correlation 0.28
Sig. (2-tailed) <0.01
Malawi
CSI score Pearson Correlation -0.30
Sig. (2-tailed) <0.01
No. of assets Pearson Correlation 0.40
Sig. (2-tailed) <0.01
No. of means (adults) Pearson Correlation 0.33
Sig. (2-tailed) <0.01
Total per cap. Cash exp. (LOG)
Pearson Correlation 0.31
Sig. (2-tailed) <0.01
We use correlation when we analyse 2 scale/continuous variables ex.
FCS with DD FCS with Kcal DD with asset index
Compare meansFCS DD
North 45 6.7
Central 38 5.1
South 27 4.2
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
Age household head
Poor FC 36
Borderline FC 45
Good FC 42
PRESENT the RESULTS
Graph
This graph aids in the interpretation and description of both dietary habits and in determining cut-offs for food consumption groups (FCGs).
Laos FCS
-
7
14
21
28
35
42
49
15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90
FCS
Cum
ula
tive C
onsum
pti
on
Fre
quency
Staple Vegetables Anim protein Oil
Sugar Fruit Pulses Milk
Graph continued
-
1.00
2.00
3.00
4.00
5.00
6.00
7.00
0 10 20 30 40 50 60 70 80 90 100
Food Consumption Score
Staple Anim protein Pulses Vegetables
Fruit Oil Sugar Milkconsumed (*) (Days/week)
(*) w eighted moving average over 7 point range
This graph shows the consumption frequency of different food groups by FCS independently and not stacked as the previous graph.
How to create the graph
1. Truncate the FCS variable 2. Run a frequency of the FCS3. Run a compare mean of the FCS and all the food groups
included in the FCS4. Export frequency and compare mean in excel5. Calculate an average of the surrounding values for each
food group (to smooth the graph).6. Use the ‘area’ or the ‘line’ graph in excel.
0%10%20%30%40%50%60%70%80%90%
100%
1 2 3 4 5
quintiles de indice de richesse
acceptable
limite
pouvre
0 7 14 21 28 35 42 49
pauvre
limite
acceptable
gro
up
es
de
con
som
ma
tio
na
lime
tair
e
Maize Rice Other Cereals Casssava, Sweet Pots, Bananas Beans, Peas Vegetables Fruits Meats Fish Eggs Milk/Yoghurt Oils/Fat/Butter Sugar, Honey, Jam
Poor and Borderline FCG
8171
81 80 8277
83 8678 80 81 84
7769
7783
91 8981
0%
5%
10%
15%
20%
25%
30%
35%
Dahuk
Ninaw
a
Sulaym
aniyah
Tamee
mErb
il
Diala
Anbar
Baghd
adBab
il
Karbala
Wass
it
Salah
Al Din
Najaf
Qadiss
ia
Mut
hana
Thi –
Qar
Miss
an
Basra
hTot
al
% o
f h
ou
seh
old
s
0102030405060708090100
FC
S
poor borderline Mean
Wealth I ndex Quintiles
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
poorconsumption
borderlineconsumption
acceptableconsumption
poorest second third fourth richest
% high dependency
mean0.36 mean
0.37 mean0.29
0%
10%
20%
poorconsumption
borderlineconsumption
acceptableconsumption
household with high dependency rate
Spearman's rho
food consumption
score
Correlation Coefficient 1
Sig. (2-tailed) .
N 24975
Correlation Coefficient -.111(**)
Sig. (2-tailed) 0
N 8877
Correlation Coefficient .378(**)
Sig. (2-tailed) 0
N 24972
Correlation Coefficient .406(**)
Sig. (2-tailed) 0
N 24971
Correlation Coefficient .343(**)
Sig. (2-tailed) 0
N 24971
Correlation Coefficient .430(**)
Sig. (2-tailed) 0
N 24934
wealth index
per capita total expenditure
per capita non foof expenditure
total_Income
food consumption score
CSI
Food Sources
Sources of foodWe 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)
Sources of food 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.
Steps
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.
Steps 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 all foods
3019 16 22 17
8
2821 15
29 24 2821
32 3426 24
17 21
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Dahuk
Ninaw
a
Sulay
man
iyah
Tamee
mErb
il
Diala
Anbar
Baghd
adBab
il
Karba
la
Wass
it
Salah
Al D
inNaj
af
Qad
issia
Mut
hana
Thi – Q
ar
Miss
an
Basra
hTot
al
p_pds p_purchase p_ow nproduction p_family other
Sources of PDS food basket
64
4033
4739
16
6252
41
6754
63
48
66 7060 58
49 49
0%
20%
40%
60%
80%
100%
Dahuk
Ninav
a
Sulay
man
iyah
Tamee
mErb
il
Diala
Anbar
Baghd
adBab
il
Karba
la
Wass
it
Salah
Al D
inNaj
af
Qad
issia
Mut
hana
Thi – Q
ar
Miss
an
Basra
hTot
al
ppds_pds ppds_purchase ppds_ownproduction ppds_family OTHER
Food sources - rural model
0% 20% 40% 60% 80% 100%
Plateau
Total
Tonle Sap
Coastal
Plains
type of source
% own producion % fishing and hunting% purchased+traded % other
Food sources - urban model
0% 20% 40% 60% 80% 100%
Plateau
Tonle Sap
Plains
Total
Coastal
Phnom Penh
type of source
% own producion % fishing and hunting% purchased+traded % other