Statistical properties of the Purchase to 

Plate Crosswalk: A comparison of Healthy 

Eating Index scores across Federal surveys

Federal Committee on Statistical Methodology

Andrea Carlson, PhD

Sabrina Young, PhD

Christopher Lowe, MS
The findings and conclusions in this presentation are 

those of the authors and should not be construed to 

represent any official USDA or U.S. Government 

determination or policy. This research was supported by 

the U.S. Department of Agriculture’s Economic 

Research Service and Center for Nutrition, Policy and 

Promotion. Findings should not be attributed to IRI.
3

Purchase to Plate Team

ERS:

Andrea Carlson*

Christopher Lowe

Elina Page

Catherine Cullinane Thomas

Mark Denbaly

Center for Nutrition Policy and Promotion 

(CNPP):

Kristin Koegel

Kevin Kuczynski

Kevin Meyers Mathieu

TusaRebecca Pannucci

*Co-Principal Investigators

Westat:

Thea Palmer Zimmerman*

Carina E. Tornow

Amber Brown McFadden

Caitlin Carter

Viji Narayana

Lindsay McDougal

Elisha Lubar

Lynnea Brumby

Raquel Brown

Maria Tamburri
4

• Current:

– Food composition

– Consumption patterns

– Dietary guidance

– Food prices

• Approved scientific methods

TFP Market basket update
5

Current Research – Retail Food Purchases

• Interactions between

– Food environment and HEI

– Local media coverage and HEI

– Self reported chronic diseases, food environment, and HEI

– HEI over time/seasonality

– Income and HEI over full distribution

– Dollar stores and HEI

• Who purchases healthy food?

• Added sugar purchases – overview
6

Purchase to Plate Suite

• Purchase to Plate Crosswalk (PPC):

– Allows scanner data users to measure 

the healthfulness of store purchases

• Purchase to Plate Price Tool (PPPT):

– Allows users to estimate custom prices 

for NHANES foods

• Purchase to Plate Ingredient Tool 

(PPIT):

– Breaks NHANES foods back into 

ingredients, based on U.S. retail food 

purchases
7

Development of the Purchase to Plate 

Crosswalk

An evolving process
8

Data Sets for the Purchase to Plate Suite

• Food Scanner data 

• USDA Food and Nutrient Database for Dietary Studies 
9

Food (at home) Scanner Data

• Retail point-of-sale (POS) data

– Purchase transaction records collected 

from store POS systems

• Household scanner data

• Household-scanned purchases

• Linked with household demographics

• Product information

• Store information

Photo credit: USDA
10

Food and Nutrient Database for Dietary Studies

(FNDDS)

• Nutrient values for foods reported consumed by What We Eat 

in America (WWEIA) participants

• A set of “recipes” for nutrient calculation

• “ingredients” in recipes are not necessarily purchasable in 

stores

• Updates every two years:

– New foods reported/developed

– Changes in methodology
11

Scanner Data 

(n=350,000)

FNDDS

(n=10,000)
12

Scanner and USDA data are different

Difference IRI Scanner Data 2015-16 FNDDS 2015-16

Primary use Market research

Support WWEIA nutrition 

monitoring and research in 

NHANES.

Number of items 500,000 12,000

Database structure Wide- many blank columns Long – not consistent information

Form of item (product weight)

Purchase form (weight can

include both edible and nonedible parts)

Raw or cooked (weight does not 

include inedible parts)
13

Methods

FNDDS

IRI

Search 

Table

Matches

Assess 

Errors

Link 

Manually
14

Linking methods

• Round 1: FNDDS 2011-12

– Completely semantic and 

probabilistic matches all at once

– Arranged both IRI and FNDDS items 

into more homogenous groups

– USDA and contractors reviewed a 

sample of matches

• Round 2: FNDDS 2013-14

– Started with semantic tables from 

previous round

– Used the IRI product dictionary 

categories

– Limited probabilistic matching to two 

rounds before going to hand-match

– Contractor reviewed every match; 

USDA reviewed a sample

• Round 3: FNDDS 2015-16

– Extreme time pressure

– Fewer UPCs, but percent of sales 

match close

– Used previous semantic table

– Developed validation plan for prices 

derived from the match

• Round 4: FNDDS 2017-18

– Assumed correct matches from 

previous round were correct in new 

round

– Price validation method continued to 

identify mismatches

• Round 5: FNDDS 2019-20

– Big changes to data

– More homogenous matching groups
15

Yield Factors are UPC-based

350,000 yield factors from:

– FNDDS

– Other government sources

– Agriculture Handbook 102

– Market Checks
16

Purchase to Plate Price Tool
17

NHANES and WWEIA

• NHANES – National Health and Nutrition Examination Survey

– 5,000 individuals each year

– Released in 2-year cycles

– Detailed medical exam

• What We Eat in America (WWEIA)

– Two 24-hour dietary recalls

– Nutrition data for foods as ingested
18

Recipes to price “as eaten” food

• Ingredients used are purchased from 

stores

• Convenience foods used

• Almost all foods prepared in 30 minutes 

or less
19

Validation of the Purchase to Plate Crosswalk

Allows scanner data users to import the nutrition data from the 

USDA Nutrition Data
20

Coverage of the PPC

Source: ERS using IRI Infoscan and The Consumer Network 2013, 2015, 2016

95 95

92

89

94

92

2013 2015 2015-16 2013 2015 2015-16

InfoScan Consumer Network

Percent of sales
21

Coverage of 2013-14 PPC in Selected Years

78

57

67

99

82

97 93 95 99 97 94

87 90

99

93

0

10

20

30

40

50

60

70

80

90

100

Beverages Frozen Refrigerated Meat Produce

% InfoScan sales

2008 2015 2016
22

Healthy Eating Index

• Measure of compliance with 

the U.S. Dietary Guidelines 

for Americans

• Used by economists, 

nutritionists, and 

epidemiologists

• Updated when the DG’S 

updated

• HEI – 2015 is the most 

current

• 100 total points

• 13 components 

• Higher score = better 

compliance

https://epi.grants.cancer.gov/hei/
23

HEI–2015 

COMPONENT

MAXIMUM STANDARD FOR 

MAXIMUM SCORE

STANDARD FOR MINIMUM 

SCORE OF ZERO

ADEQUACY (higher score indicates higher consumption)

Total Fruits 5 ≥ 0.8 cup equiv. / 1,000kcal No fruit

Whole Fruits 5 ≥ 0.4 cup equiv. / 1,000kcal No whole fruit

Total Vegetables 5 ≥ 1.1 cup equiv. / 1,000kcal No vegetables

Greens and Beans 5 ≥ 0.2 cup equiv. / 1,000kcal No dark-green vegetables, beans, or 

peas

Whole Grains 10 ≥ 1.5 ounce equiv. / 1,000kcal No whole grains

Dairy 10 ≥ 1.3 cup equiv. / 1,000kcal No dairy

Total Protein Foods 5 ≥ 2.5 ounce equiv. / 1,000kcal No protein foods

Seafood and Plant 

Proteins

5 ≥ 0.8 ounce equiv. / 1,000kcal No seafood or plant proteins

Fatty Acids 10 (PUFAs + MUFAs) / SFAs ≥ 

2.5

(PUFAs + MUFAs) / SFAs ≤ 1.2

MODERATION (higher score indicates lower consumption)

Refined Grains 10 ≤ 1.8 ounce equiv. / 1,000kcal ≥ 4.3 ounce equiv. / 1,000kcal

Sodium 10 ≤ 1.1 gram / 1,000kcal ≥ 2.0 grams / 1,000kcal

Added Sugars 10 ≤ 6.5% of energy ≥ 26% of energy

Saturated Fats 10 ≤ 8% of energy ≥ 16% of energy

https://epi.grants.cancer.gov/hei/
24

Purchase to Plate Suite

PPC – Purchase to Plate Crosswalk 

imports FNDDS/FPED data into scanner 

data

PPPT – Purchase to Plate Price Tool 

estimates prices for NHANES/WWEIA 

foods

PPPIT – Purchase to Plate Ingredient Tool 

estimates the purchase weight for 

ingredients in NHANES/WWEIA foods

USDA Nut. 

Data IRI Data

2011-12 2013

PPC

PPPT

2013-14 2015

PPC/ PPPIT

PPPT

2015-16 2015-16

PPC/ PPPIT

PPPT

2017-18 2017-18

PPC/ PPPIT

PPPT
25

Comparison of HEI-2015 Scores

2013-14 2013-14

2015-16 2015-16 2017-18 2011-12 2017-18

2013-14 2015-16

46

48

50

52

54

56

58

2011 2012 2013 2014 2015 2016 2017 2018 2019

HEI-2015

Year of data

IRI InfoScan

NHANES - FAH

NOTE: Data labels indicate the version of 

the FNDDS used for the calculation
26

Verification of 

Purchase to Plate Price Tool
27

Purchase to Plate National Average Prices for 

NHANES (PP-NAP)

• Compare average food category price changes to CPI 

– Chicken/Eggs 2015/16

– Ice Cream 2017/18

• Compare estimated expenditure using WWEIA dietary recall

– BLS Consumer Expenditure Survey 

– ERS Food Expenditure Series

• Must assume food loss and sales tax

• Annual expenditure higher in Food Expenditure Series 

– NHANES Flexible Consumer Behavior Survey

• Must assume food loss and sales tax

• Monthly expenditures within 95 percent confidence interval
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Next Steps: Statistical Properties across Time

Group foods to align with CPI categories:

• May include only a subset of prices if food not in CPI

–Foods prepared at home

• Establish which parts of PP-NAP track with inflation, 

and which do not.

2013 2015 2016 2017 2018
29

Statistical Properties Between Data Sets

• More detailed comparisons to

–BLS Consumer Expenditure Series

–ERS Food Expenditure Series

• Measures of central tendencies

• Measures of dispersion

• Measures of position

Credit: Priyam Banerjee
30

Limitations of the Purchase to Plate Suite

• PPC

– Match rate varies by 

supermarket category.

– Not tested with years other than 

creation year—check coverage 

rate of PPC and compare to 

other national estimates of HEI

• PPPT/PPIT

– Price differences might be based 

on price calculation method.

– Not fully tested with 

subdivisions of IRI data

– Generates average prices which 

are not appropriate for most 

demand models

• Retail data: 

– 15% of stores; half of all retail food 

sales recorded in the Economic Census.

– Purchases represent most large chains 

so breakdowns may not represent what 

is available at smaller and independent 

stores

– Not all stores provide data to IRI or 

allow USDA access to it

• Household data:

– Under reporting by participants

– Does not use random weight data
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Conclusions

1. HEI scores for the last two cycles (FNDDS 15-16 and 17-18) 

are consistent with findings from NHANES, suggesting we 

have a reasonable method.

2. Price changes across cycles combine inflation and methods.

3. Mean estimated daily food cost aligns with ERS data products
32

Next Steps

PPC

1. Complete the analysis for 

FNDDS 11-12, going back to 

2008.

2. Include the Consumer Network 

(household) panel

3. Examine within consistency for 

household data: Do relative 

differences along key 

demographics align with 

NHANES?

PP-NAP

1. Group foods for CPI 

comparison

2. Include Consumer Expenditure 

Survey

3. Robustness checks on ERS 

Food Expenditure Survey and 

Flexible Consumer Behavior 

Module

1. Food loss assumptions

2. Food tax assumptions
33

Thank you

• Andrea Carlson—

Andrea.Carlson@USDA.gov

• Sabrina Young —

Sabrina.Young@usda.gov

• Christopher Lowe—

Christopher.M.Lowe@usda.gov

IRI topic page:

https://www.ers.usda.gov/topics/food-markets-prices/food-prices-expenditures-and-establishments/using- proprietary-data/#iri
34

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