food consumption analysis 5 th - 9 th december 2011, rome

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