a new metrics toolbox to assess the cost and …...a new metrics toolbox to assess the cost and...

A New Metrics Toolbox to Assess the

Cost and Geographic Distribution of

Healthy Diets

Anju Aggarwal, PhD

Assistant Professor, Department of Epidemiology

Affiliated Faculty, Nutritional Sciences Program

University of Washington

World Food Center, University of California - Davis

June 3rd, 2017

Aligning food systems with dietary needs

There are multiple drivers of food choices at the consumer level:

• Economics

• Geographic

• Psychosocial

Each dimension has its metrics and measures.

The UW Food Environment Research Team

Seattle Obesity Study (SOS)

• NIDDK R01 076608-10

• SOS I 2008-2011

• SOS II 2011-2015

• SOS III 2015-2020

• King, Pierce and Yakima counties (total

N approx 3,500)

• Diets: FFQ and 24-hr recall

• Affordability: market basket

• Geolocation: GIS and GPS

• Attitudes: Questionnaire self-report

• Health outcomes: measured ht/wt

• Goal: Assess drivers of food choice; their

interactions and body weight trajectories.

Economic dimension of food access

Bureau of Labor

Statistics –

household-level

food expenditures

Are healthy foods affordable?

Do healthier diets cost more?

No data on cost of

diets at individual-

level

A novel way to estimate cost of the diet at the individual-

level by attaching prices to their diets!

• Used a standard dietary data

collection tool – Food

Frequency Questionnaires

(FFQ).

• Lowest price, non sale price

• Collected for each of the 384

foods and beverages

underlying FFQ.

Metric #1: Individual-level estimate of diet cost

Attached prices exactly the same way a nutrient vector is attached

to the FFQ

Metric #1: Individual-level estimate of diet costHow it works?

This process yields:

• Average daily intake of calories, grams, and 45 macro- and micro-nutrients for each

respondent.

• Estimated cost of the habitual diet for each respondent.

Metric #1: Individual-level estimate of diet cost

Dietary intake data

from NHANES

Nutrient composition

data FNDDS 2.0

Price data CNPP

National food price data

Energy intake

Nutrient intake

Diet cost

Diet cost

Socioeconomic status

Nutrient intakes

Food groups (HEI)

Overall diet quality (Energy density, Nutrient adequacy)

This technique brings nutrition economics to

the field of nutrition epidemiology

0

10

20

30

40

50

60

70

80

90

Q1 Q2 Q3 Q4 Q5

Pro

po

rtio

n o

f h

igh

er

inco

me a

nd

ed

ucati

on

(%

)

Quintiles of energy adjusted diet cost

Income ≥ 50KEducation ≥ college grads

Lower cost diets are more likely to be consumed by lower SES

Source: Aggarwal et al, Eur J Clin Nutr. 2011. https://www.ncbi.nlm.nih.gov/pubmed/21559042 10

* P trend < 0.0001

Diet cost linked to socioeconomic status

Source: Aggarwal et al. PLoS One 2012. http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0037533

Nutrients-to-limit cost less

70

80

90

100

110

120

130

140

150

Q1 Q2 Q3 Q4 Q5

Die

t c

os

t w

ith

Q 1

no

rma

lize

d a

t 1

00

%

Quintile of energy adjusted nutrient intakesVitamin A Vitamin D Calcium Folate

Fiber Added sugars Trans fats Total Saturated Fat

Vitamin C Magnesium Potassium Beta carotene

* P trend < 0.0001

Nutrients-to-encourage costs even more

Beneficial nutrients cost more

Nutrient rich diets tend to cost more

Diet cost linked to nutrient intakes

2.4 3.1 3.6 4.1 4.90.41.9 2.3 2.8

4.7

1.92.8

3.43.7

4.3

2

3.34.5

5

5

1.9

2

2.42.6

2.7

5.1

5.9

5.95.8

5.2

4.9

5

55

5

3.9

2.9

3.54.3

5

4.5

4.4

44.2

4.8

5.3

4

3.83.3

2.7

4.4

5.2

5.9

6.8

8.7

7.4

9.7

11.6

12.6

13.5

0

10

20

30

40

50

60

70

Q1 Q2 Q3 Q4 Q5

Adju

ste

d m

ean H

EI-

2010 s

core

Quintiles of diet cost ($/2000kcal)

Energy from SoFAAS

Refined grains

Sodium

Fatty acid ratio

Seafood & plant proteins

Total protein foods

Dairy

Whole grains

Whole fruit

Total fruit

Greens & beans

Total veg

Source: Rehm et al. Prev Med. 2015. https://www.ncbi.nlm.nih.gov/pubmed/25625693

Diets with higher HEI scores tend to cost more

Diet cost linked to healthy eating index (HEI-2010)

0

1

2

3

4

5

6

7

8

Q1 Q2 Q3 Q4 Q5

AD

JU

ST

ED

ME

AN

EN

ER

GY

DE

NS

ITY

QUINTILES OF DIET COST ($/1800KCAL)

Source: Aggarwal et al. EJCN 2011. https://www.ncbi.nlm.nih.gov/pubmed/21559042

p-value <0.0001

Energy density defined as total calories over grams of foods consumed (Kcal/g)

Energy dense diets tend to cost less

Diet cost linked to dietary energy density

68

70

72

74

76

78

80

82

84

86

88

Q1 Q2 Q3 Q4 Q5

AD

JU

ST

ED

ME

AN

EN

ER

GY

DE

NS

ITY

QUINTILES OF DIET COST ($/1800KCAL)

Source: Aggarwal et al. EJCN 2011. https://www.ncbi.nlm.nih.gov/pubmed/21559042

p-value <0.0001

Mean adequacy ratio (MAR) was defined as the truncated index of the percent of daily recommended intakes for

key nutrients. Nutrients included Vit A, C, D, E, B12, Calcium, Iron, Magnesium, Potassium, Folate and Fiber

Nutrient adequate diets tend to cost more

Diet cost linked to nutrient adequacy (MAR)

Diet cost estimated from other dietary surveys

Attached

retail

prices

Diet

records

FFQ

24 hour

recalls

US, Spain,

Japan, France

Small-scale studies,

Nurses Health

Study, NHANES

Attached prices to FFQ,

diet records, year 2000

Diet cost and diet quality in French studies

SES, diet cost and diet quality studies from the US

Attached prices to FFQ,

diet records in the US,

year 2004

Linking diet costs with

diet quality and BMI

Diet cost and diet quality evidence from Spain and Japan

Willett group attached

prices to Nurses Health

Study FFQs

Evidence from Nurses Health Study

Evidence from US National level Surveys

National prices attached

to NHANES

Recent evidence from Mexico dietary data

National prices attached

to ENSANUT 2012

Recent article from Lancet

Local prices

attached to FFQ

Geographic dimension of food accessAre healthy foods available and physically accessible to the consumer?

Supermarket within 1 mile from home

Neighborhood-centric approach

Geographic dimension of food access

Are healthy foods available and physically accessible to the consumer?

Moved to person-centric approach

https://vimeo.com/67365274

Check out this video!

Moved out of neighborhood boundaries to define

food environment

Geolocated

food sources

Source: SOS I. Developed by UFL.

Moved out of neighborhood boundaries to define

food environment

Geolocated

places of food

purchase and

consumption

Source: SOS I. Developed by UFL

Moved out of neighborhood boundaries to define

food environment

Geolocated

respondent’s

home/ work/

primary activity

Source: SOS I. Developed by UFL

Connected the two!

Source: Aggarwal et al, Am J Pub Health 2014. https://www.ncbi.nlm.nih.gov/pubmed/24625173

Opens the door to consumer-centric metrics of

geographic access

• Distance to primary food stores

• Distance to fast-food restaurants

• Distance to restaurants

• Distance to convenience stores

Home

• Distance to primary food stores

• Distance to fast-food restaurants

• Distance to restaurants

• Distance to convenience stores

Work/ primary activity

Physical distance to food shopping destinations not

linked to diets or obesity in King County

Geographic access not a barrier in King County, huge disparities in obesity prevalence still exist

4

4

8

9

13

15

17

20

22

23

25

29

38

0 10 20 30 40 50 60 70 80 90 100

Whole Foods

Madison market

Trader Joe

Met Market

PCC

Red Apple Market

QFC HI

Costco

Fred Meyer

Top Foods

Safeway

Windo Foods

Albertsons

% Non obese % ObeseSource: Seattle Obesity Study. Unpublished data

Source: Drewnowski et al, Int J Obes 2014.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3955743/

Obesity prevalence

16-22%

Obesity prevalence

36-43%

Geographic distribution of obesity at census tract level

Introduced a spatial dimension to nutrition epidemiology

Source: Drewnowski et al, Int J Obes 2014.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3955743/

Moving from State/ County level indicators down to census to neighborhood level

Source: CDC, BRFSS data 2011

https://www.cdc.gov/obesity/data/prevalence-maps.html

Source: Obesity prevalence, females, age-standardized, 2011 IHME

(http://vizhub.healthdata.org/subnational/usa)

Source: Drewnowski et al, Prev Med. 2015.

https://www.ncbi.nlm.nih.gov/pubmed/26657348

Geographic distribution of diet quality by census blocks -1st time ever!

Psychosocial dimension of food access

• Positive attitude towards eating healthy/ inexpensive/

convenient (5- point Likert scale)

• Cooking-at-home frequency per week

• Time spent cooking and cleaning after meals per day

Using standard previously validated questions from national level

surveys

Psychosocial dimension of food access

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Strongly agree

Somewhat agree

Neutral/ disagrees

Proportion meeting 5-A-Day recommendation

It is im

po

rta

nt

to b

e t

ha

t fo

od

s I

ea

t are

healthy…

Yes NoSource: Aggarwal et al. J Acad Nutr Diet 2014.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3947012/pdf/main.pdf

Positive attitude towards healthy foods was associated with much

higher intakes of fruits and vegetables

Positive attitude towards healthy foods associated with higher quality

diets irrespective of where you shop!

Source: Aggarwal et al. J Acad Nutr Diet 2014.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3947012/pdf/main.pdf

Frequent cooking-at-home associated with better compliance with dietary guidelines at no extra cost

Source: Tiwari et al, AM J Prev Med. 2017.

https://www.ncbi.nlm.nih.gov/pubmed/28256283

Good news! Healthier diets need not cost more. There is a huge variability in diet quality at every

level of cost

Source: Aggarwal et al, Prev Med. 2016.

https://www.ncbi.nlm.nih.gov/pubmed/27374943

Evidence from SOS data

The concept of nutrition resilience

Source: Aggarwal et al, 2017. http://www.fasebj.org/content/31/1_Supplement/45.8.short

Median = 72.0

Median = 8.8

Resilients!H

EI-

20

10

The concept of nutrition resilience

Evidence from NHANES

Source: Aggarwal et al, Prev Med. 2016.

https://www.ncbi.nlm.nih.gov/pubmed/27374943

Having the right attitude is linked with higher quality diets at all levels of income and education, NHANES 2007-10

40

45

50

55

60

65

<High school High school Some college College

Me

an

HE

I-2

01

0 s

co

re

Education level

Not important Somewhat important Very important

40

45

50

55

60

65

<1.3 1.3 - 1.849 1.85-3.99 ≥4.0

Me

an

HE

I-2

01

0 s

co

re

Family income-to-poverty ratio

Not important Somewhat important Very important

“When you buy foods from grocery store or supermarket, how important is nutrition”

Source: Aggarwal et al, Prev Med. 2016.

https://www.ncbi.nlm.nih.gov/pubmed/27374943

Summary

1. Toolbox of metrics provided in-depth understanding of the food environment in King County

• Individual-level estimates of diet cost (economics)

• Physical distances to food shopping destinations (geographic access)

• Food-related attitudes and practices (psychosocial access)

• Nutrition resilience (interactions)

2. Deployed the toolbox in suburban and rural Counties, WA State.

Next steps

1. Scale up the toolbox of metrics to low and middle income countries.

Source: http://www.thelancet.com/infographics/obesity-food-policy

The food system is an interconnected

network of producers, industry and

institutions. But its heart is the individual.

Lancet Obesity 2015 series. explored

Our metrics toolbox aligns with the Lancet 2015 scheme

✓ Activities in the food system

(production, distribution, storage,

marketing)

✓ Food prices and economic barriers

✓ Geographic barriers

✓ Importance of food preferences and

psychosocial barriers

✓ Interactions among all

THANK YOU

Anju Aggarwal

anjuagg@u.washington.edu