Analysis of Food Away From Home Data Collection in Auckland, NZ
Analysis of Food Away From Home
data collected in RMI
PSMB
Auckland, New Zealand
23-24 May 2019
Nathalie Troubat (FAO)
Bertrand Buffiere (SPC)
Michael Sharp (SPC)
Why should we collect FAFH?
Rapid urbanization and economic growth are typically associated with an
increase in the consumption of food away from home (FAFH) in absolute
terms as well as a share of calories and food expenditures.
Implementing traditional HCES questionnaires focused on household food
consumption at home has the risk of underestimating FAFH by missing the
increasing effect on the proportion of calories and expenditure through
food systems changes.
FAFH consumption is particularly important since food consumed outside
the home tends to be more calorie-dense and less nutrient-dense than
food consumed at home.
The increase in the amount of food consumed away tends to be faster with
increases in income.
Does it really matter?
•
Yes it does matter as impact on
the average DEC is quite
significant….
•
…But not homogenous
throughout the 5 arms…
•
…. How FAFH is captured really
matters
Recommendations to better capture FAFH
•
Data collection on food away from home should preferably be done at the
individual level
•
A separate module should be designed based on a clear definition of food
away from home and distinguish between “food prepared at home and
consumed outside” and “food prepared outside and consumed at home.”
•
Data collection should be organized around meal events, including snacks
and drinks. At a minimum, surveys should collect information on the value of
all meals consumed during a meal event away from home (breakfast, lunch,
dinner, solid snacks or drinks).
•
The meal events list should be adapted to the local context.
•
Surveys should use the
same reference period
for food away from home as
what is used in the food consumed at home module.
•
The data to estimate food away from home-related nutrient content, when
feasible, should come from other data sources integrated to the HCES, such
as a survey of food establishments or administrative data on the content of
public meals, such as those given by schools and social programmes.
Module tested in RMI
•
A separate module was developed to collect FAFH, one for diary and one for
recall
•
Reference period was 7 days as for recall and 14 days as for diary
•
Each respondent was asked to report on number of meals consumed outside
the house and amount spent if the meal was purchased or amount he/she
would spend if the meal was received for free
•
Main meals were:
•
Breakfast
•
Lunch
•
Snack
•
Diner
•
Hot drink
•
Non alcoholic drink
•
Bottled of water (in recall only)
Does the design affect number of meals reported?
•
Recall seems to over estimate the
number of meals reported on
average.
•
Main type of meals over reported in
ARM 1 are non alcoholic beverages,
snacks and hot drinks mainly
because….
…. daily quantities of some products are reported more
than once in recall by same consumer
Quantities per day per consumer
of hot drinks, non alcoholic
beverages and snacks are reported
more than once by same
consumer throughout the day
while reported only once in diary
If ARM1 tends to report higher number of meals consumed
away from home, this is also the ARM that shows more disparity
in distribution
Does the lower number of records in diary can be
attributed to a fatigue effect in reporting on FAFH ?
Apart from ARM 3 we do not
observe a special fatigue of the
respondents in filling the
section on FAFH in the diary in
second week compared to first
week
If recall report higher number of meals consumed away from
home, diaries tend to perform better in capturing on average
more consumers consuming away from home
Design also affects the average amount spent on FAFH
per day per household
If compare to ARM 2 our baseline,
PAPI low supervision diary tends to
under estimate* the average amount
spent by households.
Unbounded recall tends to better
capture FAFH compare to bounded
recall
* After deletion of three extreme values
observed in ARM 4
Does it matter to split by type of meal?
Yes, because the cost of a meal is
different from one meal to the
other*, diner consumed away
from home being more expensive
than a bottle of water consumed
away from home
* Average over all arms
Zooming at the average cost of one meal per ARM….
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Contribution of energy from meals consumed away
from home to total energy consumed
•
Lunch seems to be the meals
contributing the most to FAFH
•
Recall seem to under report the
overall share of FAFH in total
energy consumed
•
Well supervised diary performs
equally well either when data is
collected through PAPI or CAPI
•
Bounded recall does not seem
to perform better compared to
not bounded recall
Conclusion
This basic analysis confirmed that
Current survey design was under estimating impact of FAFH on
overall dietary energy consumed
A well supervised diary seem to better capture the amount of energy
consumed away from home but CAPI performs better
Bounded recall seem to under estimate the average amount of
energy consumed away from home compared to unbounded recall
recall
Important to split by meals consumed as this may help respondent to
remember the food consumed away from home and allow to better
capture FAFH
Urbanization and economic growth lead to increased consumption of food away from home, impacting calorie intake and food expenditures. Traditional household food consumption surveys may underestimate this trend. Consuming food outside the home often involves calorie-dense, less nutrient-dense options. Capturing accurate data on food away from home is crucial for understanding dietary patterns and their implications on health and expenditure. Recommendations include individual-level data collection with clear definitions, distinguishing between home-prepared and outside-prepared foods, and incorporating meal events and local context into surveys.
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Presentation Transcript
Analysis of Food Away From Home data collected in RMI PSMB Auckland, New Zealand 23-24 May 2019 Nathalie Troubat (FAO) Bertrand Buffiere (SPC) Michael Sharp (SPC)
Why should we collect FAFH? Rapid urbanization and economic growth are typically associated with an increase in the consumption of food away from home (FAFH) in absolute terms as well as a share of calories and food expenditures. Implementing traditional HCES questionnaires focused on household food consumption at home has the risk of underestimating FAFH by missing the increasing effect on the proportion of calories and expenditure through food systems changes. FAFH consumption is particularly important since food consumed outside the home tends to be more calorie-dense and less nutrient-dense than food consumed at home. The increase in the amount of food consumed away tends to be faster with increases in income.
Does it really matter? Yes it does matter as impact on the average DEC is quite significant . But not homogenous throughout the 5 arms . How FAFH is captured really matters
Recommendations to better capture FAFH Data collection on food away from home should preferably be done at the individual level A separate module should be designed based on a clear definition of food away from home and distinguish between food prepared at home and consumed outside and food prepared outside and consumed at home. Data collection should be organized around meal events, including snacks and drinks. At a minimum, surveys should collect information on the value of all meals consumed during a meal event away from home (breakfast, lunch, dinner, solid snacks or drinks). The meal events list should be adapted to the local context. Surveys should use the same reference period for food away from home as what is used in the food consumed at home module. The data to estimate food away from home-related nutrient content, when feasible, should come from other data sources integrated to the HCES, such as a survey of food establishments or administrative data on the content of public meals, such as those given by schools and social programmes.
Module tested in RMI A separate module was developed to collect FAFH, one for diary and one for recall Reference period was 7 days as for recall and 14 days as for diary Each respondent was asked to report on number of meals consumed outside the house and amount spent if the meal was purchased or amount he/she would spend if the meal was received for free Main meals were: Breakfast Lunch Snack Diner Hot drink Non alcoholic drink Bottled of water (in recall only)
Does the design affect number of meals reported? Recall seems to over estimate the number of meals reported on average. Main type of meals over reported in ARM 1 are non alcoholic beverages, snacks and hot drinks mainly because . ARM1 ARM2 ARM3 ARM4 ARM5 Average number of meals per day 1.18 0.74 0.68 0.57 0.79
. daily quantities of some products are reported more than once in recall by same consumer Quantities per day per consumer of hot drinks, non alcoholic beverages and snacks are reported more than once by same consumer throughout the day while reported only once in diary Item description mean(ave_qu~y) min(ave_qu~y) max(ave_qu~y) hot drinks away from home 1 1 1 non alcoholic drinks away from home 1 1 1 snacks away from home 1 1 1
If ARM1 tends to report higher number of meals consumed away from home, this is also the ARM that shows more disparity in distribution 10 8 number_meal_pd 6 4 2 0 ARM 1 ARM 2 ARM 3 ARM 4 ARM 5
Does the lower number of records in diary can be attributed to a fatigue effect in reporting on FAFH ? Apart from ARM 3 we do not observe a special fatigue of the respondents in filling the section on FAFH in the diary in second week compared to first week
If recall report higher number of meals consumed away from home, diaries tend to perform better in capturing on average more consumers consuming away from home
Design also affects the average amount spent on FAFH per day per household If compare to ARM 2 our baseline, PAPI low supervision diary tends to under estimate* the average amount spent by households. Unbounded recall tends to better capture FAFH compare to bounded recall * After deletion of three extreme values observed in ARM 4
Does it matter to split by type of meal? Yes, because the cost of a meal is different from one meal to the other*, diner consumed away from home being more expensive than a bottle of water consumed away from home * Average over all arms
Zooming at the average cost of one meal per ARM. how the FAFH section was how the FAFH section was administered matters as administered matters as for instance in ARM1 the for instance in ARM1 the design matters as in ARM design matters as in ARM 1 breakfasts and dinner 1 breakfasts and dinner are reported as being are reported as being more more costly costly compared to compared to the other ARMs the other ARMs
Contribution of energy from meals consumed away from home to total energy consumed Lunch seems to be the meals contributing the most to FAFH Recall seem to under report the overall share of FAFH in total energy consumed Well supervised diary performs equally well either when data is collected through PAPI or CAPI Bounded recall does not seem to perform better compared to not bounded recall ARM 1 ARM 2 ARM 3 ARM 4 ARM 5 Average contribution of FAFH to total energy consumed 9.3% 13.7% 12.7% 11.5% 6.4%
Conclusion This basic analysis confirmed that Current survey design was under estimating impact of FAFH on overall dietary energy consumed A well supervised diary seem to better capture the amount of energy consumed away from home but CAPI performs better Bounded recall seem to under estimate the average amount of energy consumed away from home compared to unbounded recall recall Important to split by meals consumed as this may help respondent to remember the food consumed away from home and allow to better capture FAFH
