Assessing and Planning Nutrient Intakes

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 byHarvestPlus researchers in cooperation with local collaborators inPhilippines, 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 Mouraand 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 theprevalence of inadequate or excessive consumptions.Planning of interventions to address inadequacies at the populationlevel.Evaluation of the effectiveness of interventions.Nutrition epidemiology.

Inferences draw from food consumption surveys can lead to incorrectinferences unless the appropriate methodology is used for statisticalanalyses.


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


Daily versus usual nutrient intake

In most large-scale studies (including national surveys) foodconsumption data are collected using 24-hour recall instruments.

The 24-hr recalls capture (or try to capture) food consumption for anindividual during the previous 24-hour period.

Participants report the food they consumed, in what amounts and onwhat occasion.

Foods are “mapped” into nutrients and other components using foodcomposition tables.

Lots of errors creep in: under(over)-reporting of certain foods, portionsizes, incomplete food composition tables, interview method.....


Daily versus usual nutrient intake (cont’d)

Policy makers, practitioners and researchers are interested in usualnutrient and usual food intake and in distributions of usual nutrientand food intakes.

By usual we typically mean average intake over a large enough period.


Daily versus usual nutrient intake (cont’d)

Usual intakes (of nutrients or foods or other components) could beestimated directly if daily intakes were observed over long enoughperiods on each person.

Except in experimental, small scale studies, we cannot observe dailyintake over long enough periods because of:

costrespondent burden and consequent attrition.

The survey in Indonesia collected only one recall from eachparticipant. For Philippines and Bangladesh we have two independentrecalls for each sample person.

From this scarce information, we wish to draw inferences both at theindividual and at the group level.

We can produce credible estimates at the group level.Draw inferences at the individual level at your own risk!


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 meanintake for the person is the mean of the Yij over the di days and isdenoted Yi .

The usual intake of the nutrient by person i is denoted yi , andconceptually:

yi = E (Yij |i).

From a public policy perspective, we are interested in estimating thedistribution of the usual intakes, f (y).

Since the variability among the yi in a group represents thebetween-person variance in intake of the nutrient, we expect thatf (y) will have a variance that reflects the person-to-person variancein usual intake.


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 anunbiasd 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 goodestimator of f (y).


How informative are two days of data?


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 removeits effect when estimating the distribution of usual intakes.

The goal is to get an estimated distribution whose variance reflectsonly differences between persons.


Consequences of not adjusting distributions

What happens if we decide not to adjust distributions and just workwith one 24-hr recall or with the mean of two days?

Some examples follow.


Iron among Philippines women aged 31-50

0 5 10 15 20 25 30

0.00

0.05

0.10

0.15

0.20

Philippines women aged 31−50

Iron intake (mg/d)

Den

sity

Observed one 24−hr recallEstimated usual intake


Vitamin A among Philippino women aged 31-50

0 500 1000 1500 2000

0.00

000.

0010

0.00

200.

0030

Philippines women aged 31−50

Vitamin A intake (ug RAE/d)

Den

sity

Observed one 24−hr recallEstimated usual intake


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 meanand variance and shape, and thus the correct “tails”.

It relies on a simple measurement error model proposed by NRC in1986.

The model describes the association between daily intake and usualintake.

Daily intakeij = usual intakei + errorij .

More formally:Yij = yi + eij ,

where eij ∼ (0, σ2w ) and yi ∼ (µ, σ2b) with i = 1, ..., n persons andj = 1, ..., d days of intake data per person.


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: theday-to-day variability in daily intakes within each person and theperson-to-person variability in usual intakes:

Var(Yij) = Var(yi + eij) = σ2b + σ2

w .

Further, under the same simple model:

Var(Yi ) = σ2b +σ2wd,

so the distribution f (Y ) has a variance that is too large (by the

amount σ2wd ) and therefore tails that extend too far out.



Roughly, we estimate f (y) by f (y), where y is a weighted averagegiven by:

yi = r Yi + (1− r)Y ,

where Y is the observed mean intake in the population and

r =Var(y)

Var(Yi )=

σ2bσ2b + σ2w/d

.

The factor r approaches 1 when σ2w/d is close to zero and thenyi −→ Yi .

The factor r approaches 0 when σ2w/d is large relative to σ2b and thenyi −→ Y .

This makes intuitive sense: when daily intakes are variable, a few daysof data provide little information about a person’s usual intake. Wemight be better off using the population average as our best “guess”for the person.



In addition to the basic capabilities, the ISU method:

Accounts for the effect of complex survey designs, so thatsample-based inferences can be generalized to the population fromwhich the sample was drawn.Eliminates the effect of nuisance factors such as day of week, interviewmethod, interview sequence, others.Develops a transformation into the normal scale that is flexible and canbe used for all nutrients with no modifications.Estimates the correct back-transformation into the original scale of thedata, so that results can be expressed in the original units.


Prevalence of inadequate intakes

The prevalence of inadequacy for a nutrient is defined as theproportion of persons in a group whose usual intakes of the nutrientdo not meet their requirements for the nutrient.

Requirements are unobservable, so prevalence cannot be estimateddirectly.

Beaton (1994) and Carriquiry (1999) showed that under someconditions, prevalence can be estimated as the proportion of personsin the group with usual intakes below the average requirement of thenutrient in the group.

The EAR (Estimated Average Requirement) has been calculated formost nutrients for persons separated by gender, age and physiologicstatus.

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 tocompute the proportion of persons with intakes below the EAR, wewould be overestimating prevalence.


The EAR cut-point method - hypothetical example

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0 50 100 150

010

2030

4050

Requirements vs. intakes

Usual intake

Req

uire

men

t

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EAR RDA


Planning intakes

Given consumption (per person and per day) of a suitable foodvehicle, we can model nutrient intake at different target fortificationlevels.

For this project, we have rice consumption in g for each person andeach day.

For different target nutrient concentrations in rice (in ppm) we cancompute 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 thefortification scenarios.


Fortification levels

For this project, we modeled intakes for the following targetfortification 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 forbeta-carotene.

Zinc bioavailability was assumed to be 22%.


Design of simulation

We did not have any information about adoption rates and factorsthat may affect it in the different countries.

Bio-fortified rice looks different from the highly polished, whitevarieties that in some areas are considered most desirable.

We considered adoption rates between 10% and 70% and for eachscenario we proceeded as follows:

1 Within age, gender group and country, randomly select X% of thepopulation and declare them adopters.

2 For the adopters, substitute actual daily rice intake with fortified riceintake.

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 ofinadequacy.

5 Repeat all steps 10 times, each time selecting a different randomsample of adopters.

Average results over the 10 replicates.


Software

PC-SIDE implements the ISU method and is freely available since theearly 2000s.

Mac-SIDE is forthcoming (maybe by November).

The WHO provided the funds two years ago to develop a moresophisticated and easier to use program that is now becoming popular.

The new program is called IMAPP (Intake Modeling Assessment andPlanning Program).

Both programs can be downloaded from www.side.iastate.edu


The WHO Intake Monitoring Assessment and Planning Program

(WHO IMAPP)

Uses: Interprets group or population nutrient intake data in terms of • prevalence of inadequate and excessive intakes.

Estimates if a given fortification strategy will be safe and ef-• ficacious for all population groups consuming a fortified food vehicle.

Developed for WHO by:Dr. Alicia Carriquiry, Iowa State University, • Dr. Lindsay Allen, USDA, ARS Western Human Nutrition Re-• search Center, University of California, DavisDr. Suzanne Murphy, University of Hawaii•


Preliminary results - Indonesia

Survey includes women 14 - 50 and children 5 years of age andyounger.

Children were divided into four age groups:1 0 - 6 months of age2 7 - 12 months of age3 1 - 3 years of age4 4 - 5 years of age.

Women were divided into six groups defined by age and physiologicalstatus. Age groups were:

1 14 - 19 years2 20 - 30 years3 31 - 50 years.

Each age group was sub-divided into two groups: pregnant and notpregnant.


0 5 10 15 20

0.00

0.05

0.10

0.15

Indonesia − children 1−3 years

Target ppm of iron

Den

sity

2 ppm6 ppm12 ppm16 ppm22 ppm


0 200 400 600 800 1000 1200 1400

0.00

000.

0010

0.00

20

Vitamin A − Indonesia children 1−3 years

Usual vit A intake (RAE)

Den

sity

4 ppm8 ppm12 ppm20 ppmEAR = 210 mg RAE


0 200 400 600 800 1000 1200 1400

0.00

000.

0010

0.00

20

Vitamin A − Indonesia children 4−5 years

Usual vit A intake (RAE)

Den

sity

4 ppm8 ppm12 ppm20 ppmEAR = 210 mg RAE


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0.2

0.4

0.6

0 5 10 15 20PPM

Pre

vale

nce

0.1

0.2

0.3

0.4

0.5

0.6

adoption

Indonesia Women 31−50 years, not pregnant


●

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●●

0 5 10 15 20

0.0

0.1

0.2

0.3

0.4

0.5

0.6

Indonesia − Children

Target ppm of beta−carotene

Pre

vale

nce

of v

it A

inad

equa

cy●

●

●

●

●

●●

1 − 3 years4 − 5 years


0 5 10 15 20 25

0.00

0.10

0.20

0.30

Zinc − Indonesia children 1−3 years

Usual zinc intake (mg)

Den

sity

16 ppm24 ppm30 ppm45 ppm75 ppm100 ppmiZinc EAR 2 mg/dIOM EAR 3.4 mg/d


0 5 10 15 20 25

0.0

0.1

0.2

0.3

Zinc − Indonesia children 4−5 years

Usual zinc intake (mg)

Den

sity

16 ppm24 ppm30 ppm45 ppm75 ppm100 ppmiZinc EAR 4 mg/dIOM EAR 5.5 mg/d


●

●●●

●● ● ●

0 20 40 60 80 100

0.0

0.2

0.4

0.6

0.8

Indonesia children 1−3 years

Target ppm of zinc

Pre

vale

nce

of z

inc

inad

equa

cy

●

●

●●

●

●●

●

iZinc EAR 2 mg/dIOM EAR 3.4 mg/d


●

●

●●

●

●● ●

0 20 40 60 80 100

0.0

0.2

0.4

0.6

0.8

Indonesia children 1−3 years

Target ppm of zinc

Pre

vale

nce

of z

inc

inad

equa

cy●

●

●

●

●

●

●

●

iZinc EAR 2 mg/dIOM EAR 3.4 mg/d


0 2 4 6 8 10 12

0.0

0.1

0.2

0.3

0.4

Bangladesh − children 1−3 years not breastfed

Target ppm of iron

Den

sity



●●

●

●

●

●

●

●

●

0 5 10 15 20

0.0

0.2

0.4

0.6

0.8

1.0

Bangladesh − Children aged 1−3 years not breasfed

Target iron ppm

Pre

vale

nce

of ir

on in

adeq

uacy

●

●

●

●

●

●

●●

●

10% bioavailable18% bioavailable


0 2 4 6 8 10 12 14

0.0

0.1

0.2

0.3

0.4

Bangladesh − children 4−5 years, not breastfed

Target ppm of iron

Den

sity



●●

●●

●

●

●

●

●

0 5 10 15 20

0.0

0.2

0.4

0.6

0.8

1.0

Bangladesh − Children aged 4−5 years not breastfed

Target iron ppm

Pre

vale

nce

of ir

on in

adeq

uacy

●

●

●

●

●

●

●●

●

10% bioavailable18% bioavailable


Some preliminary thoughts

Biofortification of rice with iron, zinc and beta-carotene is promising.

Iron biofortification appears to be the least effective, andbeta-carotene seems to be most effective.

The issue of iron bio-availability is complex and deserved moreinvestigation; iron absorption critically affects status.


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Assessing and Planning Nutrient Intakes

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