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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.
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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 • Explore the module of food consumption • Calculate the FCS • Graph the result • Create the Food consumption score groups • Validate the FCS with other indicators • Analyze the sources of food
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 • 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.
Calculation steps • 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.
FCS FCS = astaplexstaple+ apulsexpulse+ avegxveg+ afruitxfruit+ aanimalxanimal+ asugarxsugar + adairyxdairy+ aoilxoil Where,FCS Food consumption scorexi 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
Graph This graph aids in the interpretation and description of both dietary habits and in determining cut-offs for food consumption groups (FCGs).
How to create the graph • 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’ graph in excel
Graph cont’ 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 • Use the same steps from the graph above; • 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.
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 ofstaple 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) consumptionof 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 • 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.
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?
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 • 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 • 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 + …… • COMPUTE the total sources of food totsource = ownproduction + fishing + purchase + traded + borrow + exc_labor + exc_item + gift + food_aid +other.
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.
