Introduction to IMAPP
(and other topics)
Alicia Carriquiry, David Osthus
Outline
Estimation of usual nutrient intake distributions
The DRI and prevalence of inadequate intakes
Brief introduction to PC-SIDE
Planning intakes for groups
IMAPP capabilities (Alicia)
IMAPP demonstration (David)
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Alicia Carriquiry - Iowa State University
11/18/2012
Daily intakes and usual intakes
In many surveys and other studies, we use 24-hour recalls
to measure daily nutrient intake for individuals.
The daily intake of a nutrient varies from day-to-day
within an individual.
From a public health perspective, we are interested in
usual intakes and not on daily intakes.
Usual intake is the long-run average intake for a person.
The usual intake varies from person to person in a group.
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Alicia Carriquiry - Iowa State University
11/18/2012
Daily intakes and usual intakes (cont)
We can express daily intake as a sum of usual intake plus
a deviation from usual intake:
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Daily intake = Usual Intake + Error
For example, a person may have a usual intake of folate
equal to 300 DFE per day, but three daily measurements
for that same person may be 100 DFE, 450 DFE and 245
DFE.
The average of the three days is 265 DFE.
The SD of the three daily intakes is 219 DFE - this
reflects the within-person variability in folate intake.
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Alicia Carriquiry - Iowa State University
11/18/2012
Daily intakes and usual intakes (cont)
If we collect daily intakes for a group of persons, the
variance of observed intakes will have two components:
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Variation in daily intakes =
Within-person + Between-person variability
To estimate usual intake for a single person, we need to
average many daily intakes.
Example: We observe daily intakes for two persons over
100 days. Mean intakes are 64 and 40 units.
Within-person SDs are 15 and 6, so variances are 225
and 36, with average within-person variance of 130.5.
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Alicia Carriquiry - Iowa State University
11/18/2012
Example: two persons, 100 days
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Alicia Carriquiry - Iowa State University
11/18/2012
Relative size of SD
Age group
Females 14-18y
Nutrient
Ratio
3.54
3.13
1.13
Prog (g/k)
0.46
0.60
0.66
357.93
346.78
1.03
Vit B12
5.39
4.15
1.39
Prog (g/k)
0.42
0.53
0.79
681.32
514.06
1.33
Vit A
7
Between SD
Vit B12
Vit A
Females 19-30y
Within SD
Alicia Carriquiry - Iowa State University
11/18/2012
Distribution of usual intakes
At the group level, however, we can estimate the
distribution of usual intakes with one or two days of
observation for each person.
What data do we need?
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Daily intakes for a sample of persons.
At least for some, we need a second observation.
Second observation should be (hopefully) taken on a nonconsecutive day.
There are several methods to estimate usual intake
distributions. Oldest is NRC (1986).
We use the ISU Method (Nusser et al. 1996; IOM,
2000) today.
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Alicia Carriquiry - Iowa State University
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Removing the within-person variance
The within-person variability in intakes is a nuisance
(molesta!).
To estimate important quantities such as the proportion
of persons with inadequate intakes or with excessive
intakes, we need to use the usual intakes.
If we estimate usual intake distributions using a single day
of intake or the mean of a few days of intake, we overestimate the prevalence of inadequacy (and the
prevalence of excess).
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Alicia Carriquiry - Iowa State University
11/18/2012
Children 4-8 NHANES 2005-2006
Mean
50th
75th
90th 95th
One day
102
82
136
208
248
Adjusted
102
98
122
148
165
One day
9
8
11
14.6
16.9
Adjusted
8.9
8.7
10.2
11.7
12.8
Vit C
Zinc
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11/18/2012
Vitamin B6 – Women 31-50
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Alicia Carriquiry - Iowa State University
11/18/2012
Serum 25(OH)D NHANES 2001-2006
<30
nmol/L
30-39
nmol/L
40-49
nmol/L
50-75
nmol/L
>75
nmol/L
Unadjusted
8.2
9.3
14.4
47.2
20.8
Adjusted
4.9
9.8
17.1
48.8
19.4
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Alicia Carriquiry - Iowa State University
11/18/2012
Example dataset
ID Number
Day
Sex
Vitamin C
Iron
126701
1
F
98.5
14.1
126701
2
F
56.1
0.5
145665
1
F
76.3
8.9
133289
1
M
124.6
7.0
133289
2
M
101.2
19.9
134678
1
M
32.7
24.0
156700
1
F
65.2
6.1
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Alicia Carriquiry - Iowa State University
11/18/2012
The ISU Method and PC-SIDE
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A statistical approach to estimate usual intake
distributions when we have daily intakes from a group of
persons.
We need a second day on at least a sub-sample.
Software: PC-SIDE.
PC-SIDE is distributed free of charge and can be found in
Documentation can also be found on that site.
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Alicia Carriquiry - Iowa State University
11/18/2012
What does PC-SIDE do?
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PC-SIDE removes the nuisance within-person variance
from daily intakes.
It also adjusts daily intake for the effects of:
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Day of week
Season
Any other factor that affects nutrient intakes.
It produces estimates of usual intake distributions.
Estimates prevalence of nutrient inadequate intakes or of
excessive intakes.
In complex surveys, it uses the survey weights to produce
unbiased estimates of means, variances, percentiles….
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Alicia Carriquiry - Iowa State University
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The DRIs to assess intakes
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The DRIs (Dietary Reference Intakes) defined by the IOM
and Health Canada refer to nutrient requirements.
There are four DRIs:
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EAR: Estimated Average Requirement is the typical
requirement of a nutrient of a person in an age-sex group. The
EAR meets the needs of 50% of individuals in the group.
RDA: Required Daily Allowance is a level of usual intake that
exceeds the requirements of over 97% of persons in the group.
UL: Upper Tolerable Level is the highest level of usual intake
that is likely to be safe for almost everyone in the group.
AL: Adequate Intake is established when there is not enough
information about requirements to set an EAR.
Alicia Carriquiry - Iowa State University
11/18/2012
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Alicia Carriquiry - Iowa State University
11/18/2012
DRIs to assess intakes of groups
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The only two DRIs that are useful for groups are the EAR
and the UL.
The RDA should NEVER be used to assess group intakes.
Beaton (1994) and Carriquiry (1999) proposed a simple
method to estimate prevalence of inadequate intakes
called the EAR cut-point method.
The proportion of persons in a group with intakes below their
requirements is approximately equal to the proportion of
persons with intakes below the EAR for the nutrient.
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Alicia Carriquiry - Iowa State University
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Planning interventions
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From survey or other representative data we get a
baseline estimate of the nutritional status of our
population.
We may find that the prevalence of inadequate intakes in
some sub-populations is unacceptably high.
Food fortification with micronutrients can improve health
outcomes in populations.
Guidelines on Food Fortification with Micronutrients was
published in 2006 by WHO and FAO.
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Alicia Carriquiry - Iowa State University
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The idea behind planning
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Alicia Carriquiry - Iowa State University
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Planning intakes (cont’d)
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Establish a target prevalence of inadequacy.
Decide on a set of possible food vehicles.
Define the “dose” or the amount of the nutrient to be
added per 100 g of the vehicle.
Simulate the intakes that may be observed after
fortification assuming that consumption patterns do not
change due to cost, taste, etc.
Evaluate whether the dose under consideration suffices
to achieve the target prevalence.
Alicia Carriquiry - Iowa State University
11/18/2012
Baby Example
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We have baseline daily intakes of vitamin A and of three
possible vehicles: sugar, vegetable oil and wheat flour.
The limit of fortification is:
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700 mg RAE / 100 g of sugar
2500 mg RAE / 100 g of vegetable oil
240 mg RAE / 100 g of wheat flour.
Retinol is added as the fortificant.
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Alicia Carriquiry - Iowa State University
11/18/2012
Baby example (cont’d)
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If a person consumes 149 mg RAE of vitamin A on day 1
at baseline and also consumes 15 g of sugar, 8 g of oil and
0 g of wheat flour, the additional intake of vitamin A in
RAE would be:
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From sugar: 700 x 15 / 100 = 105 mg RAE
From oil: 2500 x 8 / 100 = 200 mg RAE
From wheat flour: 240 x 0 / 100 = 0 mg RAE
If we fortify the three vehicles, then the person’s intake
on day 1 would be :
Baseline + vit A from sugar + vit A from oil + vit A from flour:
149 + 105 + 200 + 0 = 454 mg RAE.
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Alicia Carriquiry - Iowa State University
11/18/2012
Baby example (cont’d)
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For each person, we construct the “new” daily intake
values.
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Alicia Carriquiry - Iowa State University
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Baby Example (cont’d)
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With those new intake values, we re-compute the
prevalence of inadequate intakes as usual.
If the new prevalence is approximately equal to the target
prevalence, then we are done.
If the new prevalence is smaller or larger than the target,
we reconsider the amount of the fortificant added to
each vehicle or the mix of vehicles and try again.
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Alicia Carriquiry - Iowa State University
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Planning intakes (cont’d)
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Additional data needs: daily intake of possible food
vehicles by all persons in the sample.
Given a user-chosen target prevalence, we wish to
compute:
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The gap between baseline intake levels and intake levels that
would meet the target prevalence.
The approximate amount of the nutrient to be added to 100 g
of a vehicle to achieve the target prevalence.
Alicia Carriquiry - Iowa State University
11/18/2012
Estimating the Gap
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The gap between baseline and target is the difference
between:
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The EAR (the percentile at baseline proportion of usual
inadequate intakes) and
The quantile corresponding to the desired (or target)
proportion of usual inadequate intakes.
E.g. if observed prevalence is 23% and target prevalence is
10%, gap equals EAR – 10th quantile of usual intake.
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Alicia Carriquiry - Iowa State University
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Fortifying vehicles
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Planner chooses the number of units of the nutrient that
is added to 100 g of the vehicle. This is the dose.
Then computes additional nutrient intake per person per
day:
Additional intake = g vehicle x dose / 100.
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Nutrient intake after fortification:
Intake after fortification = Baseline intake + Additional intake
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Alicia Carriquiry - Iowa State University
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Fortifying vehicles (cont’d)
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Initial “guess” of optimal dose:
Dose ~ Gap x 100 / Average vehicle consumption (g).
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E.g., if gap = 3 mg and average consumption of vehicle per
day is 10 g, then initial guess for dose is
Dose = 3 x 100 / 10 = 30 mg per 100 g of vehicle.
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It is easier to guess the optimal dose when vehicle is
consumed in approximately the same amounts by all
persons in a group.
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Alicia Carriquiry - Iowa State University
11/18/2012
Fortifying vehicles (cont’d)
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A food scientist determines what is feasible
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Technically, accounting for taste, manufacturing constraints, etc.
Economically, from a cost-wise perspective.
At present, we have the tools to consider one vehicle at a
time, but ideally we wish to construct a possibilities region,
composed of all combinations of vehicles and doses that
meet the goals.
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Alicia Carriquiry - Iowa State University
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IMAPP
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IMAPP (Intake Monitoring, Assessment and Planning
Program) is software that can be used to
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Estimate usual nutrient intake distributions at baseline
Plan fortifications.
The program was developed by Alicia Carriquiry, (Iowa
State U), Suzanne Murphy (U of Hawaii) and Lindsay Allen
(UC Davis), in collaboration with Bruno de Benoit and
Lisa Rogers (WHO).
Funding was provided by WHO.
The program implements the methods in the 2006
WHO/FAO report Guidelines on Food Fortification with
Micronutrients .
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Alicia Carriquiry - Iowa State University
11/18/2012
Uses of IMAPP
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Assesses group intake data in terms of prevalence of
inadequate and excessive intakes.
Estimates whether a given fortification strategy will be
safe and effective for all population groups consuming a
fortified food vehicle.
Enables practitioners to choose between different
fortification strategies on the basis of safety and efficacy.
(By safety, we mean proportion of persons with usual
intakes below the UL.)
Screen 1
Screen 2
Program characteristics
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IMAPP’s engine is PC-SIDE.
IMAPP does everything that PC-SIDE does, but also
permits modeling fortification strategies.
It produces estimates the prevalence of inadequate and
excessive intakes before and after different levels of
fortification.
It is based on recommendations from WHO/FAO 2006
report Guidelines on Food Fortification with Micronutrients.
Adjusts the nutrient intake distributions for day-to-day
variability in intakes using the ISU method.
Program characteristics (cont’d)
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User-friendly, and does not require expertise in statistical
calculations.
Supplies default values for many of the statistical and
nutritional parameters, including nutrient requirements
and external variance estimates.
Clearly documents and explains all steps in a user’s
manual.
User provides:
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Daily dietary intake data (e.g., from 24-hr recalls or food
records) for each person in the sample.
Age, gender and reproductive status (pregnant, lactating
or neither) for each person in the sample.
Nutrient intakes as well as daily intakes of potential food
vehicles for each person.
User can select:
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Bioavailability of dietary iron and of iron used to fortify.
The form of the nutrient (e.g., folate or folic acid, retinol or
carotenoids).
The DRIs for the nutrient (if different from default values).
User provides (cont’d):
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Two or more days of dietary data for at least a
representative subsample; if not, default values are
suggested for day-to-day variance estimates.
Persons’ body weights (in kg) to assess protein intake; if
not, default weight values are suggested.
Bioavailability factors for iron and zinc can be supplied by
user; if not, default values are suggested.
Nutrient standards used by IMAPP
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Software uses harmonized nutrient requirements
obtained by compiling EARs from the US/Canada DRIs,
RNIs from the FAO/WHO tables and other information
from the literature.
The user may choose to input own values or may use the
default “harmonized” values in the program, which were
developed for each gender/age/reproductive status group.
Safe upper levels of intake are based largely on the ULs
from the US/Canada DRIs.
To conclude…
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Today is the official launch of IMAPP!
IMAPP is free to anyone who wishes to use it.
Next presentation: David will do several step-by-step
demonstrations on how to use the program.
The program may have some bugs – PLEASE let us know
whether you encounter problems when you use it.
When you download the program, we will keep your
name and email address. We will be able to contact you
to tell you about updates, corrections and other things
that may be of interest.
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Alicia Carriquiry - Iowa State University
11/18/2012
THANKS FOR YOUR ATTENTION
alicia@iastate.edu
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Alicia Carriquiry - Iowa State University
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