food consumption analysis
DESCRIPTION
Food consumption analysis. Food Security Indicators Training Bangkok 12-17 January 2009. Objectives. To describe the analysis of food consumption To describe the analysis on food sources To discuss experiences/problems related with the analysis of food consumption. Steps. - PowerPoint PPT PresentationTRANSCRIPT
Food consumption analysisFood consumption analysis
Food Security Indicators Training
Bangkok 12-17 January 2009
Objectives
• To describe the analysis of food consumption
• To describe the analysis on food sources • To discuss experiences/problems related
with the analysis of food consumption
Steps
1. Explore the module of food consumption2. Calculate the FCS3. Graph the result 4. Create the Food consumption score
groups5. Validate the FCS with other indicators6. Analyze the sources of food
Definitions
Dietary Dietary diversitydiversity
The number of individual foods or food groups consumed over a reference period
Food frequency Food frequency Number of days (in the past week) that a specific food item has been consumed by a household
Household Household Food Food ConsumptionConsumption
The consumption patterns (frequency * diversity) of households over the last seven days
Food consumption module
FC module info
Information: Weekly frequency of foods and food groups Sources of foods Numbers of meals
Indicators: → FCS – 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.The FCS can be considered as a proxy of food access and food security.
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 steps
1. 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.
Calculation steps
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.
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 ITEMSFOOD ITEMS Food groupsFood groups Weight 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
Weights
Food groups Weight J ustification
Main staples 2 Energy dense, protein content lower and poorer quality (PER less) than legumes, micro-nutrients
(bound by phytates).
Pulses 3 Energy dense, high amounts of protein but of lower quality (PER less) than meats, micro-nutrients (inhibited by phytates), low fat.
Vegetables 1 Low energy, low protein, no fat, micro-nutrients
Fruit 1 Low energy, low protein, no fat, micro-nutrients
Meat and fish 4
Highest quality protein, easily absorbable micro-nutrients (no phytates), energy dense, fat. Even
when consumed in small quantities, improvements to the quality of diet are large.
Milk 4
Highest quality protein, micro-nutrients, vitamin A, energy. However, milk could be consumed only in very small amounts and should then be
treated as condiment and therefore re-classification in such cases is needed.
Sugar 0.5 Empty calories. Usually consumed in small
quantities.
Oil 0.5 Energy dense but usually no other micro-
nutrients. Usually consumed in small quantities
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
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’ graph in excel
Graph cont’
-
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. Use the same steps from the graph above;2. Use the ‘line’ graph in excel.
FCS thresholds
Once the FCS is calculated, the thresholds for the FCGs 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 – 21Poor food
consumption 0-28
21.5 - 35 Borderline food consumption
28.5 - 42
>35.5Acceptable 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 2121 comes from an expected daily consumption ofdaily consumption of staplestaple and vegetables.vegetables.
» (frequency * weight, 7 * 2 = 14)+(7 * 1 = 7).
The value 3535 comes from an expected daily consumption of daily consumption of staple and vegetables complemented bystaple and vegetables complemented by a frequent (4 day/week) consumption of oil and pulses. 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 north and the 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’ !!!!
Validation of the FCS
• Run verifications of the FCS and FCGs by comparing them to other proxy indicators of food consumption, food access, and food security:
Cash expenditures, % expenditures on food, food sources, CSI, wealth index,number of meals eaten per day, etc.
Which is the analysis that we should use to compare 2 continuous variables?
Correlations
Correlations with FCS comparing FCS to other food security proxies
Burundi
Pearson Correlation 0.31 kcal/capita/day
Sig. (2-tailed) <0.01
Pearson Correlation -0.27 CSI score
Sig. (2-tailed) <0.01
Pearson Correlation -0.11 % total cash expenditures on food Sig. (2-tailed) <0.01
Pearson Correlation 0.24 asset index
Sig. (2-tailed) <0.01
Pearson Correlation 0.28 total cash monthly expenditures (LOG) Sig. (2-tailed) <0.01
Malawi
Pearson Correlation -0.30 CSI score
Sig. (2-tailed) <0.01
Pearson Correlation 0.40 No. of assets
Sig. (2-tailed) <0.01
Pearson Correlation 0.33 No. of means (adults)
Sig. (2-tailed) <0.01
Pearson Correlation 0.31 Total per cap. Cash exp. (LOG) Sig. (2-tailed) <0.01
Proxy for food security
If the FCS captures several elements of food consumption, food access, and food security
(such as in the previous slide’s example) FCS is an adequate proxy for CURRENT
food security
Sources of food
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)
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.
Example
S ourc es of food
3 419
7
25 23 24
2
195
22
95 9072
8961 71
53 9473
9365
11 7 3
125 2 7 1
1222
0
10
20
30
40
5060
70
80
90
100
Urban Urban R ural Urban R ural Urban R ural Urban R ural Urban R ural
P hnomP enh
P lains Tonle S ap P lateau C oas tal Total
% own produc ion % purc has e% fis hing and hunting % traded % borrowed % exc hange of labor for food% exc hange of items for food % gift% food aid % exc hange other
Questions?Questions?
Some examples
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
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