Assessing and Planning Nutrient Intakes
Alicia Carriquiry
Department of Statistics
September 30, 2015

Thanks to...
Most of the data used in this presentation were collected by
HarvestPlus researchers in cooperation with local collaborators in
Philippines, Indonesia and Bangladesh.
The study is part of the program to fortify rice with beta-carotenes,
zinc and iron carried out by researchers in IRRI.
My three main collaborators in this project were Fabiana de Moura
and Mourad Moursi (HarvestPlus) and Gerard Barry (IRRI).
Alicia Carriquiry (ISU) September, 2015 2 / 42

Analysis of dietary intake data
Quantitative methods play a critical role in
Collection of food consumption data.
Monitoring of food and nutrient intake and estimation of the
prevalence of inadequate or excessive consumptions.
Planning of interventions to address inadequacies at the population
level.
Evaluation of the effectiveness of interventions.
Nutrition epidemiology.
Inferences draw from food consumption surveys can lead to incorrect
inferences unless the appropriate methodology is used for statistical
analyses.
Alicia Carriquiry (ISU) September, 2015 3 / 42

Outline
Daily versus usual nutrient intake
The ISU method to estimate intake distributions
Estimating prevalence of inadequacy
Planning: Ex-ante analysis of bio-fortifying rice
Preliminary results from Indonesia, Philippines and Bangladesh
Some final thoughts
Alicia Carriquiry (ISU) September, 2015 4 / 42

Daily versus usual nutrient intake
In most large-scale studies (including national surveys) food
consumption data are collected using 24-hour recall instruments.
The 24-hr recalls capture (or try to capture) food consumption for an
individual during the previous 24-hour period.
Participants report the food they consumed, in what amounts and on
what occasion.
Foods are “mapped” into nutrients and other components using food
composition tables.
Lots of errors creep in: under(over)-reporting of certain foods, portion
sizes, incomplete food composition tables, interview method.....
Alicia Carriquiry (ISU) September, 2015 5 / 42

Daily versus usual nutrient intake (cont’d)
Policy makers, practitioners and researchers are interested in usual
nutrient and usual food intake and in distributions of usual nutrient
and food intakes.
By usual we typically mean average intake over a large enough period.
Alicia Carriquiry (ISU) September, 2015 6 / 42

Daily versus usual nutrient intake (cont’d)
Usual intakes (of nutrients or foods or other components) could be
estimated directly if daily intakes were observed over long enough
periods on each person.
Except in experimental, small scale studies, we cannot observe daily
intake over long enough periods because of:
cost
respondent burden and consequent attrition.
The survey in Indonesia collected only one recall from each
participant. For Philippines and Bangladesh we have two independent
recalls for each sample person.
From this scarce information, we wish to draw inferences both at the
individual and at the group level.
We can produce credible estimates at the group level.
Draw inferences at the individual level at your own risk!
Alicia Carriquiry (ISU) September, 2015 7 / 42

A bit of notation
We use Yij to denote the intake of a nutrient by person i on day j.
If the person reports daily intake on di days, then the observed mean
intake for the person is the mean of the Yij over the di days and is
denoted ¯Yi .
The usual intake of the nutrient by person i is denoted yi , and
conceptually:
yi = E(Yij |i).
From a public policy perspective, we are interested in estimating the
distribution of the usual intakes, f (y).
Since the variability among the yi in a group represents the
between-person variance in intake of the nutrient, we expect that
f (y) will have a variance that reflects the person-to-person variance
in usual intake.
Alicia Carriquiry (ISU) September, 2015 8 / 42

Distribution of usual intakes
Of interest: f (y), the distribution of usual nutrient or of food intakes.
Assume (for now) that the mean intake ¯Yi for the ith person is an
unbiasd estimate of that person’s usual intake.
If so, is the distribution of ¯Yi in the population a good estimate f (y)?
No...
Turns out that for small d, ¯Yi is quite noisy and f ( ¯Y ) is not a good
estimator of f (y).
Alicia Carriquiry (ISU) September, 2015 9 / 42

How informative are two days of data?
Alicia Carriquiry (ISU) September, 2015 10 / 42

Within and between-person variance in intake
Daily intake of a nutrient is subject to two sources of variability:
Differences in usual intake from one person to the other.
Differences in daily intake within a person (day-to-day).
The day-to-day variance in intake is a nuisance, and we must remove
its effect when estimating the distribution of usual intakes.
The goal is to get an estimated distribution whose variance reflects
only differences between persons.
Alicia Carriquiry (ISU) September, 2015 11 / 42

Consequences of not adjusting distributions
What happens if we decide not to adjust distributions and just work
with one 24-hr recall or with the mean of two days?
Some examples follow.
Alicia Carriquiry (ISU) September, 2015 12 / 42

Iron among Philippines women aged 31-50
0 5 10 15 20 25 30
0.000.050.100.150.20
Philippines women aged 31−50
Iron intake (mg/d)
Density
Observed one 24−hr recall
Estimated usual intake
Alicia Carriquiry (ISU) September, 2015 13 / 42

Vitamin A among Philippino women aged 31-50
0 500 1000 1500 2000
0.00000.00100.00200.0030
Philippines women aged 31−50
Vitamin A intake (ug RAE/d)
Density
Observed one 24−hr recall
Estimated usual intake
Alicia Carriquiry (ISU) September, 2015 14 / 42

The ISU method to estimate intake distributions
The ISU method (Nusser, Carriquiry, Dodd and Fuller, JASA, 1996)
estimates usual nutrient intake distributions with the correct mean
and variance and shape, and thus the correct “tails”.
It relies on a simple measurement error model proposed by NRC in
1986.
The model describes the association between daily intake and usual
intake.
Daily intakeij = usual intakei + errorij .
More formally:
Yij = yi + eij ,
where eij ∼ (0, σ2
w ) and yi ∼ (µ, σ2
b) with i = 1, ..., n persons and
j = 1, ..., d days of intake data per person.
Alicia Carriquiry (ISU) September, 2015 15 / 42

The ISU method (cont’d)
Under the model,
1 The observed mean intake is unbiased for usual intake:
E(Yij ) = yi .
2 The observed variability in daily intake has two components: the
day-to-day variability in daily intakes within each person and the
person-to-person variability in usual intakes:
Var(Yij ) = Var(yi + eij ) = σ2
b + σ2
w .
Further, under the same simple model:
Var( ¯Yi ) = σ2
b +
σ2
w
d
,
so the distribution f ( ¯Y ) has a variance that is too large (by the
amount σ2
w
d ) and therefore tails that extend too far out.
Alicia Carriquiry (ISU) September, 2015 16 / 42

The ISU method (cont’d)
Roughly, we estimate f (y) by f (˜y), where ˜y is a weighted average
given by:
˜yi = r ¯Yi + (1 − r) ¯Y ,
where ¯Y is the observed mean intake in the population and
r =
Var(y)
Var( ¯Yi )
=
σ2
b
σ2
b + σ2
w /d
.
The factor r approaches 1 when σ2
w /d is close to zero and then
˜yi −→ ¯Yi .
The factor r approaches 0 when σ2
w /d is large relative to σ2
b and then
˜yi −→ ¯Y .
This makes intuitive sense: when daily intakes are variable, a few days
of data provide little information about a person’s usual intake. We
might be better off using the population average as our best “guess”
for the person.
Alicia Carriquiry (ISU) September, 2015 17 / 42

The ISU method (cont’d)
In addition to the basic capabilities, the ISU method:
Accounts for the effect of complex survey designs, so that
sample-based inferences can be generalized to the population from
which the sample was drawn.
Eliminates the effect of nuisance factors such as day of week, interview
method, interview sequence, others.
Develops a transformation into the normal scale that is flexible and can
be used for all nutrients with no modifications.
Estimates the correct back-transformation into the original scale of the
data, so that results can be expressed in the original units.
Alicia Carriquiry (ISU) September, 2015 18 / 42

Prevalence of inadequate intakes
The prevalence of inadequacy for a nutrient is defined as the
proportion of persons in a group whose usual intakes of the nutrient
do not meet their requirements for the nutrient.
Requirements are unobservable, so prevalence cannot be estimated
directly.
Beaton (1994) and Carriquiry (1999) showed that under some
conditions, prevalence can be estimated as the proportion of persons
in the group with usual intakes below the average requirement of the
nutrient in the group.
The EAR (Estimated Average Requirement) has been calculated for
most nutrients for persons separated by gender, age and physiologic
status.
The EAR is a quantile of the usual intake distribution.
If we were to use daily intakes or the mean of a few daily intakes to
compute the proportion of persons with intakes below the EAR, we
would be overestimating prevalence.
Alicia Carriquiry (ISU) September, 2015 19 / 42

The EAR cut-point method - hypothetical example
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0 50 100 150
01020304050
Requirements vs. intakes
Usual intake
Requirement
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EAR RDA
Alicia Carriquiry (ISU) September, 2015 20 / 42

Planning intakes
Given consumption (per person and per day) of a suitable food
vehicle, we can model nutrient intake at different target fortification
levels.
For this project, we have rice consumption in g for each person and
each day.
For different target nutrient concentrations in rice (in ppm) we can
compute the corresponding units of the nutrient per 100 g of rice.
To forecast the effect of the fortification, we
Compute the nutrient intakes for each person on each survey day.
Re-estimate the intake distributions at each new level of intake.
Re-calculate the prevalence of inadequacy under each of the
fortification scenarios.
Alicia Carriquiry (ISU) September, 2015 21 / 42

Fortification levels
For this project, we modeled intakes for the following target
fortification levels:
Iron : 4, 6, 8, 12, 16, 20 and 22 ppm.
Beta-carotene : 4, 6, 8, 12, 16 and 20 ppm.
Zinc : 20, 24, 28, 30, 45, 60, 75 and 100 ppm.
Prevalence of inadequacy of iron intakes was estimated assuming 10%
and 18% absorption.
To compute vitamin A µg of RAE we used a 3.8:1 conversion for
beta-carotene.
Zinc bioavailability was assumed to be 22%.
Alicia Carriquiry (ISU) September, 2015 22 / 42

Design of simulation
We did not have any information about adoption rates and factors
that may affect it in the different countries.
Bio-fortified rice looks different from the highly polished, white
varieties that in some areas are considered most desirable.
We considered adoption rates between 10% and 70% and for each
scenario we proceeded as follows:
1 Within age, gender group and country, randomly select X% of the
population and declare them adopters.
2 For the adopters, substitute actual daily rice intake with fortified rice
intake.
3 Recompute the adopters’ daily intake of zinc, iron and vitamin A.
4 Mix the adopters back into the population, and re-estimate the usual
intake distributions of the three nutrients, and prevalence of
inadequacy.
5 Repeat all steps 10 times, each time selecting a different random
sample of adopters.
Average results over the 10 replicates.
Alicia Carriquiry (ISU) September, 2015 23 / 42

Software
PC-SIDE implements the ISU method and is freely available since the
early 2000s.
Mac-SIDE is forthcoming (maybe by November).
The WHO provided the funds two years ago to develop a more
sophisticated and easier to use program that is now becoming popular.
The new program is called IMAPP (Intake Modeling Assessment and
Planning Program).
Both programs can be downloaded from www.side.iastate.edu
Alicia Carriquiry (ISU) September, 2015 24 / 42

Uses:
Interprets group or population nutrient intake data in terms of• Alicia Carriquiry (ISU) September, 2015 25 / 42

Preliminary results - Indonesia
Survey includes women 14 - 50 and children 5 years of age and
younger.
Children were divided into four age groups:
1 0 - 6 months of age
2 7 - 12 months of age
3 1 - 3 years of age
4 4 - 5 years of age.
Women were divided into six groups defined by age and physiological
status. Age groups were:
1 14 - 19 years
2 20 - 30 years
3 31 - 50 years.
Each age group was sub-divided into two groups: pregnant and not
pregnant.
Alicia Carriquiry (ISU) September, 2015 26 / 42

0 5 10 15 20
0.000.050.100.15
Indonesia − children 1−3 years
Target ppm of iron
Density
2 ppm
6 ppm
12 ppm
16 ppm
22 ppm
Alicia Carriquiry (ISU) September, 2015 27 / 42

0 200 400 600 800 1000 1200 1400
0.00000.00100.0020
Vitamin A − Indonesia children 1−3 years
Usual vit A intake (RAE)
Density 4 ppm
8 ppm
12 ppm
20 ppm
EAR = 210 mg RAE
Alicia Carriquiry (ISU) September, 2015 28 / 42

0 200 400 600 800 1000 1200 1400
0.00000.00100.0020
Vitamin A − Indonesia children 4−5 years
Usual vit A intake (RAE)
Density 4 ppm
8 ppm
12 ppm
20 ppm
EAR = 210 mg RAE
Alicia Carriquiry (ISU) September, 2015 29 / 42

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0.2
0.4
0.6
0 5 10 15 20
PPM
Prevalence
0.1
0.2
0.3
0.4
0.5
0.6
adoption
Indonesia
Women 31−50 years, not pregnant
Alicia Carriquiry (ISU) September, 2015 30 / 42

q
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q
0 5 10 15 20
0.00.10.20.30.40.50.6
Indonesia − Children
Target ppm of beta−carotene
PrevalenceofvitAinadequacy
q
q
q
q
q
q
q
1 − 3 years
4 − 5 years
Alicia Carriquiry (ISU) September, 2015 31 / 42

0 5 10 15 20 25
0.000.100.200.30
Zinc − Indonesia children 1−3 years
Usual zinc intake (mg)
Density 16 ppm
24 ppm
30 ppm
45 ppm
75 ppm
100 ppm
iZinc EAR 2 mg/d
IOM EAR 3.4 mg/d
Alicia Carriquiry (ISU) September, 2015 32 / 42

0 5 10 15 20 25
0.00.10.20.3
Zinc − Indonesia children 4−5 years
Usual zinc intake (mg)
Density 16 ppm
24 ppm
30 ppm
45 ppm
75 ppm
100 ppm
iZinc EAR 4 mg/d
IOM EAR 5.5 mg/d
Alicia Carriquiry (ISU) September, 2015 33 / 42

q
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q q q
0 20 40 60 80 100
0.00.20.40.60.8
Indonesia children 1−3 years
Target ppm of zinc
Prevalenceofzincinadequacy
q
q
qq
q
q
q
q
iZinc EAR 2 mg/d
IOM EAR 3.4 mg/d
Alicia Carriquiry (ISU) September, 2015 34 / 42

q
q
q
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q
q q
0 20 40 60 80 100
0.00.20.40.60.8
Indonesia children 1−3 years
Target ppm of zinc
Prevalenceofzincinadequacy
q
q
q
q
q
q
q
q
iZinc EAR 2 mg/d
IOM EAR 3.4 mg/d
Alicia Carriquiry (ISU) September, 2015 35 / 42

0 2 4 6 8 10 12
0.00.10.20.30.4
Bangladesh − children 1−3 years not breastfed
Target ppm of iron
Density
2 ppm
6 ppm
12 ppm
16 ppm
22 ppm
Alicia Carriquiry (ISU) September, 2015 36 / 42

q
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0 5 10 15 20
0.00.20.40.60.81.0
Bangladesh − Children aged 1−3 years not breasfed
Target iron ppm
Prevalenceofironinadequacy
q
q
q
q
q
q
q
q
q
10% bioavailable
18% bioavailable
Alicia Carriquiry (ISU) September, 2015 37 / 42

0 2 4 6 8 10 12 14
0.00.10.20.30.4
Bangladesh − children 4−5 years, not breastfed
Target ppm of iron
Density
2 ppm
6 ppm
12 ppm
16 ppm
22 ppm
Alicia Carriquiry (ISU) September, 2015 38 / 42

q
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0 5 10 15 20
0.00.20.40.60.81.0
Bangladesh − Children aged 4−5 years not breastfed
Target iron ppm
Prevalenceofironinadequacy
q
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q
q
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q
10% bioavailable
18% bioavailable
Alicia Carriquiry (ISU) September, 2015 39 / 42

Some preliminary thoughts
Biofortification of rice with iron, zinc and beta-carotene is promising.
Iron biofortification appears to be the least effective, and
beta-carotene seems to be most effective.
The issue of iron bio-availability is complex and deserved more
investigation; iron absorption critically affects status.
Alicia Carriquiry (ISU) September, 2015 40 / 42