Article2.pdf

Where do food desert residents buy most of their junk food?
Supermarkets

Christine A Vaughan1,*, Deborah A Cohen1, Madhumita Ghosh-Dastidar2,
Gerald P Hunter3 and Tamara Dubowitz3
1RAND Corporation, 1776 Main Street, PO Box 2138, Santa Monica, CA 90407–2138, USA:
2RAND Corporation, Arlington, VA, USA: 3RAND Corporation, Pittsburgh, PA, USA

Submitted 23 March 2016: Final revision received 19 August 2016: Accepted 1 September 2016: First published online 5 October 2016

Abstract
Objective: To examine where residents in an area with limited access to healthy
foods (an urban food desert) purchased healthier and less healthy foods.
Design: Food shopping receipts were collected over a one-week period in 2013.
These were analysed to describe where residents shopped for food and what
types of food they bought.
Setting: Two low-income, predominantly African-American neighbourhoods with
limited access to healthy foods in Pittsburgh, PA, USA.
Subjects: Two hundred and ninety-three households in which the primary food
shoppers were predominantly female (77·8 %) and non-Hispanic black (91·1 %)
adults.
Results: Full-service supermarkets were by far the most common food retail outlet
from which food receipts were returned and accounted for a much larger
proportion (57·4 %) of food and beverage expenditures, both healthy and
unhealthy, than other food retail outlets. Although patronized less frequently,
convenience stores were notable purveyors of unhealthy foods.
Conclusions: Findings highlight the need to implement policies that can help to
decrease unhealthy food purchases in full-service supermarkets and convenience
stores and increase healthy food purchases in convenience stores.

Keywords
Diet

Food desert
Food receipts

Food retail environment

Unhealthy diet is a modifiable risk factor for chronic
conditions such as diabetes(1), cancer(2) and CVD(3), and
has been highlighted as a major public health problem(4,5).
Although widespread across the USA, an unhealthy diet is
more common among low-income populations(6), parti-
cularly those who reside in low-income neighbourhoods
in which access to healthy, affordable foods is lacking,
i.e. ‘food deserts’(7).

Guided by the assumption that geographic access is a
major factor underlying a poor diet(7), recent policy
initiatives have invested hundreds of millions of dollars
into food deserts to increase access to healthy foods(8).
Understanding household food purchasing behaviour, i.e.
the purchase of foods from a variety of sources, including
but not limited to grocery stores, neighbourhood and
convenience stores, and restaurants(9,10), could illuminate
the role of geographic access in actual dietary intake.
Specifically, analysis of where residents purchase healthier
(i.e. fruits and vegetables) and less healthy (i.e. high in
sugar, salt or energy) foods can provide a more complete
understanding of whether and how the neighbourhood

food environment might best be modified to improve food
purchasing behaviour. For example, policies may need to
be modified to facilitate purchase of healthy foods in retail
outlets where healthy foods are under-represented and/or
stymie purchase of unhealthy foods in outlets where
unhealthy foods are over-represented.

Of the various methods for assessing household food
purchasing behaviour, including food shopping receipts,
home food inventories, Universal Product Code (UPC) bar
scanning and self-reported shopping behaviour in surveys,
food receipts have some key advantages(9). Food receipts
capture foods from a wider variety of sources, including
both stores and restaurants, whereas home food inven-
tories and UPC bar scanning capture only foods purchased
in stores and/or eaten at home. In addition, food receipts
are better suited for assessment of food purchasing
behaviour over a greater period of time, thereby affording
more stable estimates of food purchasing behaviour.
Food receipts are also advantageous over self-reported
shopping behaviour in that they do not depend on the
accuracy of participants’ recall. Recent empirical research

Public Health Nutrition: 20(14), 2608–2616 doi:10.1017/S136898001600269X

*Corresponding author: Email [email protected] © The Authors 2016

http://crossmark.crossref.org/dialog/?doi=10.1017/S136898001600269X&domain=pdf

affirms the viability and validity of food receipts as a
source of data on household food purchasing behaviours
across a variety of food sources(9).

The collection of food receipts to study household food
purchasing behaviour is a relatively recent development.
One of the main recent sources of data on household food
purchasing behaviour, the 2010 Nielsen Homescan Panel
Survey data, suggests that residents of lower-income
neighbourhoods purchase less healthy foods than their
higher-income counterparts(11). However, data for that
study were collected using the UPC scanning method and
supplemented by self-report data. That study has the
notable strengths of a large consumer panel and detailed
data on food purchases, but the data do not include food
purchases from sources other than stores (e.g. restaurants)
and under-represent poor consumers. Other research has
analysed receipts data, but, to our knowledge, none have
focused on residents of an urban food desert(10,12).

We sought to fill these gaps by collecting food receipts
data to examine household food purchasing behaviour of
low-income African Americans residing in a food desert.
We present a detailed description of food purchasing
patterns in this population, focusing on the types of food
retail outlets (e.g. full-service grocery store, convenience
store) residents patronized and the kinds of foods and
beverages purchased (e.g. fruits, vegetables, sweets, salty
snacks) from these venues.

Methods

Design and sample
The food receipts data analysed for the present study were
collected as part of the Pittsburgh Hill/Homewood
Research on Eating, Shopping, and Health (PHRESH),
a 5-year study of residents and their neighbourhood
environment in two predominantly African-American,
low-income ‘food deserts’ in Pittsburgh, PA, USA. The
study sought to understand the effect on residents of
eliminating a food desert: in one of the neighbourhoods, a
new full-service supermarket was slated to open. PHRESH
study participants were recruited from a random sample of
households drawn from a complete list of residential
addresses generated by the Pittsburgh Neighborhood and
Community Information System (a detailed description of
sampling procedures is provided elsewhere(13)). The final
sample consisted of 1372 households where the primary
food shopper in each household was interviewed and
administered an in-person baseline questionnaire in
their home between May and December 2011. The study
protocol was approved by the Institutional Review Board
of the institution where the study was conducted.

Approximately two years after completion of the
PHRESH baseline interview, data collectors returned to
the same households to conduct a different household
interview with the same primary household shopper for a

separate but related study of physical activity (PHRESH
Plus, or the Pittsburgh Hill/Homewood Research on
Neighborhoods, Exercise and Health). At the same time
this interview was administered, data collectors asked
participants to collect their food shopping receipts from all
household food purchases, including those in stores and
restaurants, and wear an accelerometer to record their
physical activity over the course of one week. At the end
of the week, data collectors returned to collect the food
shopping receipts and accelerometer; participants were
compensated an additional $US 25 (above and beyond
$US 15 compensation for completing the household
interview) for participating in this component of the study.
These data, including the food receipts, were collected
prior to the opening of the new supermarket whose
influence was evaluated in the PHRESH study.

Of 1372 primary household food shoppers who
completed the PHRESH baseline interview, 982 (71·6 %)
also participated in PHRESH Plus, and 644 of these
participants returned household food shopping receipts.*

Due to the labour-intensive nature of entering food
receipts data and constrained resources, we randomly
selected 300 participants from the sampling frame of 644
participants. Of the 300 participants, seven were excluded
due to incomplete information on the receipts (i.e. food
items were undiscernible), resulting in a final analytic
sample size of 293 participants in the food receipts data
analysis.

Household interviews
Household interviews included questions on participants’
sociodemographic characteristics (e.g. age, gender, race/
ethnicity, marital status, educational attainment) and
objective measurements of height and weight. Missing
values on income were imputed with the software IVE-
Ware in SAS macros version 0.2 (2009; Software Survey
Methodology Program at the University of Michigan’s
Survey Research Center, Institute for Social Research, Ann
Arbor, MI, USA). Adjusted income was computed as a ratio
of household income and size.

Food receipts
Key receipt data elements, including the names and
locations of stores and restaurants for which receipts were
returned and the names, quantities and pre-tax costs of
food and beverage items purchased, were manually
entered by research assistants. To ensure reliable
extraction of receipt characteristics, a coding protocol with
standard definitions for data elements was created and all
research assistants were trained to follow it. Research

* We included receipts only from participants who had completed both
the PHRESH and PHRESH Plus baseline interviews, so that we would be
able to examine household food purchasing behaviour based on receipts
data in relation to outcomes assessed in both the PHRESH and PHRESH
Plus interviews in subsequent studies.

Where food desert residents buy junk food 2609

assistants were initially required to demonstrate fidelity to
the protocol by coding five receipts correctly according to
the independent coding done by a researcher or senior
research assistant who had already demonstrated fidelity
to the protocol. After this initial training period, fidelity to
the coding protocol was ensured by having a senior coder
check a random sample of 10 % of receipts entered by
each assistant.

Because of wide variability in the types of foods and
beverages purchased and the level of detail reported on
receipts across food retail outlets, one food or beverage
‘item’ was generally defined as the smallest packaged unit
for purchase. For example, all of the following would have
been counted as a single item: a carton of eggs, a bag of
potato chips and an eight-pack of Pepsi cans. Thus, the
actual quantity in a single item varies tremendously across
different foods and beverages. For the vast majority (80 %)
of items, specific quantities in a single unit were not listed
on the receipt and are thus unknown. In lieu of data on the
quantity of food and beverage items, the cost of food and
beverage items serves as a rough proxy of the quantity of
item(s) purchased.

After raw receipt data elements were recorded, all types
of food items purchased in stores were classified into one
of several mutually exclusive categories. Because some
food items consisted of multiple types of food, items were
assigned to a single category according to the following
hierarchy: prepackaged or takeaway/eating-out entrée;
sweetened baked goods; ice cream or gelato; candy;
condiments, dips and gravy; sweets; salty snacks;
potatoes; meat; eggs; cheese; yoghurt; butter, margarine or
spread; whole grains; other type of grain (refined or
not specified); nuts and seeds; fruit; beans and peas;
vegetables; and other. These categories were later
aggregated to form larger categories for analysis: empty
calories (sweets, salty snacks, butter, margarine,
shortening, condiments, dips and gravy); protein (meat,
eggs, nuts and seeds, beans and peas); grains (whole,
refined or not specified); vegetables (including potatoes);
fruits; dairy (cheese, yoghurt); prepackaged and
takeaway/eating-out entrées; and other. A similar process
was followed for beverages using the following categories:
sugar-sweetened beverages; milk; fruit/vegetable juice;
water; artificially sweetened or low-calorie beverages;
coffee and tea; condiments (e.g. creamer); alcohol; and
specialty drinks (e.g. latté, smoothie). Food and beverage
items purchased in restaurants were not listed in sufficient
detail to permit classification into finer-grained categories.

We initially classified stores into one of eleven
categories. Classifications were based on definitions from
the Food Marketing Institute and the North American
Industry Classification System and confirmed with our
Community Advisory Boards, comprised of key resident
stakeholders within each neighbourhood. To simplify, we
reduced these categories to the following four categories
of stores: (i) full-service supermarkets, which include

grocery stores run by nationally or regionally recognized
chains; (ii) mass merchandising and discount grocery
stores, which include supercentres (e.g. Walmart, Target),
wholesale clubs (e.g. Sam’s Club, Costco) and discount
grocery stores, which offer a large assortment of
low-priced food items (e.g. Save A Lot); (iii) convenience
stores, which include small chain stores such as those at
gas stations (e.g. Get Go, AM/PM), neighbourhood stores
(i.e. small individual/family-owned stores), drug stores
and dollar stores, which offer a limited assortment of
low-priced and perishable items (e.g. Family Dollar); and
(iv) other stores, such as meat or seafood markets and
specialty grocery stores (e.g. Whole Foods). Restaurants
were also classified into one of the following mutually
exclusive categories: (i) fast-food restaurant; (ii) restaurant
with table service; (iii) buffet or cafeteria; (iv) bar, tavern
or lounge; (v) coffee shop; and (vi) other.

Statistical analyses
Given the paper’s descriptive purpose, we report univariate
descriptive statistics to characterize the sociodemographic
and other characteristics of the primary household food
shoppers who returned receipts. We then present univariate
descriptive statistics for food purchases to characterize
where participants did their food shopping, what types of
foods and beverages they purchased, and, finally, where
they purchased different types of foods and beverages. We
did not conduct any tests of significance for two main
reasons: (i) we had no particular hypotheses for this
descriptive paper; and (ii) where we do compare two or
more groups descriptively (e.g. amount of household food
expenditures on a particular food type in different store
types), unbalanced, small sample sizes limited our power to
detect statistically significant differences between groups.
Analyses were conducted in the statistical software package
SAS version 9.4 of the SAS System for Windows.

Results

Participant characteristics
As shown in Table 1, most participants in the receipts
sample were female (77·8 %) and non-Hispanic black
(91·1 %); on average, participants were 55·06 (SD 15·17)
years old. Roughly half of the sample had a high-school
education or less (50·5 %). Slightly less than half of the
sample reported that a recipient of the Supplemental
Nutrition Assistance Program (SNAP) resided in the
household (46·1 %) and slightly more than half of the
sample reported access to a vehicle when needed
(55·0 %); on average, per capita annual household income
was $US 13 913·20 (SD $US 11 879·80). Most participants
were single, divorced, separated or widowed (81·3 %) and
reported no children residing in the household (75·1 %).
On average, participants reported having made roughly

2610 CA Vaughan et al.

three visits to the main food store at which they had done
their major food shopping in the past month (mean 2·93
(SD 0·83)) and having made roughly two visits to
other stores (besides the main store) for major food
shopping in the past month (mean 1·90 (SD 1·13)). Roughly
three-quarters (78·8 %) of participants were considered to
be overweight or obese based on BMI.

To assess response bias, we compared the 293 partici-
pants in the final analytic receipts sample with the total
sample of 982 participants in the parent study who were
eligible for inclusion in the receipts analysis (i.e. had the
opportunity to return their food shopping receipts and
completed both the PHRESH and PHRESH Plus baseline
interviews). As shown in Table 1, the receipts sample
very closely resembled the parent study sample on most
characteristics. Exceptions were residence of a recipient of
SNAP benefits in the household and per capita annual
household income: having a SNAP benefits recipient
residing in the household was slightly less common in the
receipts sample (46·1 %) than in the parent sample (52·1%)
and participants in the receipts sample appeared to have
slightly higher per capita annual household income (mean
$US 13913·20 (SD $US 11 879·80)) than those in the
parent sample (mean $US 12 790·52 (SD $US 12 919·44)). In
addition, the samples appeared to differ slightly on marital
status, such that those in the receipts sample were slightly
more likely to have been widowed, divorced or separated

(45·1%) and slightly less likely never to have been married
(36·2%) than those in the parent sample (widowed,
divorced or separated, 40·7 %; single, never married, 41·8 %);
however, the samples had very similar proportions of
participants who were currently married or living with their
partner (receipts sample, 18·8%; parent sample, 17·5%).

Food retail outlets where household food purchases
were made
Across the 293 households, 879 receipts were returned.
On average, each household returned three receipts
(SD 2·51). As shown in Fig. 1, a greater proportion of
households returned receipts from stores (93·2 %) than
restaurants (27·0 %). Similarly, at the aggregate level, stores
accounted for a much larger proportion of total food and
beverage expenditures (92·6 %) than restaurants (7·4 %).

Among stores, full-service supermarkets were by far the
most common store type from which households returned
food receipts (65·5 %) and accounted for a much larger
proportion of food and beverage expenditures (57·4 %)
than any other food retail channel. After full-service
supermarkets, receipts were most commonly returned
from convenience stores (42·7 %), followed by mass
merchandising/discount grocery stores (31·7 %) and other
store types (10·2 %). After full-service supermarkets, the
greatest proportion of total food and beverage

Table 1 Characteristics of primary household food shoppers in the receipts study and the parent study, Pittsburgh, PA, USA, 2013

Receipts sample* (N 293) Parent sample* (N 982)

Characteristic n % n %

Female 228 77·8 751 76·5
Non-Hispanic black 267 91·1 891 91·5
Highest level of education completed
Less than high school 38 13·0 132 13·4
High school 110 37·5 380 38·7
Some college 98 33·5 327 33·3
College 47 16·0 143 14·6

SNAP recipient in household 135 46·1 512 52·1
Owns or has access to a vehicle 161 55·0 549 56·2
Marital status
Married or living with partner 55 18·8 171 17·5
Single, never married 106 36·2 408 41·8
Widowed, divorced or separated 132 45·1 398 40·7

Children under 18 years old in household 73 24·9 269 27·4
Weight status
Not overweight or obese (BMI < 25·0 kg/m2) 59 20·1 211 21·7 Overweight (BMI = 25·0–29·0 kg/m2) 90 30·7 287 29·5 Obese (BMI ≥ 30·0 kg/m2) 141 48·1 475 48·8 Mean SD Mean SD Age (years) 55·06 15·17 54·14 16·12 Per capita annual household income ($US) 13 913·20 11 879·80 12 790·52 12 919·44 Number of main food store visits, past month 2·93 0·83 2·87 0·85 Number of other food store visits, past month 1·90 1·13 1·93 1·12 SNAP, Supplemental Nutrition Assistance Program; PHRESH, Pittsburgh Hill/Homewood Research on Eating, Shopping, and Health; PHRESH Plus, Pittsburgh Hill/Homewood Research on Neighborhoods, Exercise and Health. *Participants in the receipts sample are primary household food shoppers whose household food shopping receipts were included in the final analytic sample for the present paper. They are a subset of parent study participants, who completed the baseline interviews for both PHRESH and PHRESH Plus and were invited to return food shopping receipts. Where food desert residents buy junk food 2611 expenditures was accounted for by mass merchandising/ discount grocery stores (21·6 %), followed by other store types (7·1 %) and convenience stores (6·6 %). Among restaurants, more households returned receipts from fast-food restaurants (18·1 %), followed by restau- rants with table service (8·9 %) and buffets or cafeterias (5·8 %). Paralleling these trends, fast-food restaurants accounted for a greater proportion of food and beverage expenditures (3·4 %) than restaurants with table service (2·4 %) and other types of restaurants (1·6 %). Types of foods and beverages purchased in stores We also examined the types of foods and beverages purchased in stores (see Table 2). The great majority of expenditures in stores were for foods (87·9 %) rather than beverages (12·1 %). More than one-third of household food expenditures were for foods high in protein (e.g. meats; 38·0 %). Foods that consist primarily of empty calories (e.g. sweets, salty snacks) collectively accounted for the second largest proportion of household food expenditures (22·5 %). All other categories represented less than 10 % of household food expenditures. We saw a similar pattern when we examined food expenditures on a household level. We observed extensive variability in food expenditures across households. In the full sample, sugar-sweetened beverages accounted for the greatest share of household beverage expenditures in stores (40·2 %), followed by milk (16·8 %) and fruit/vegetable juice (11·1 %). All other beverage types represented less than 10 % of beverage expenditures. The average household beverage expenditures followed a similar trend and, like food, the standard deviation for each beverage category indicated substantial variability across households in beverage expenditures. Store types where healthy and unhealthy foods were purchased To determine where purchases of unhealthy and healthy foods were made, we conducted a more granular analysis of expenditures for specific types of unhealthy and healthy 5.8 1.6 8.9 2.4 18.1 3.4 10.2 7.1 42.7 6.6 31.7 21.6 65.5 57.4 0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100.0 % Other restaurants Restaurants with table service Fast-food restaurants Other stores Convenience stores Mass merchandising/discount grocery stores Full-service supermarkets Ty p e o f st o re o r re st a u ra n t Fig. 1 The percentage of households that returned receipts from stores and restaurants ( ) and the percentage of total food expenditures by store and restaurant type ( ) among households in a food desert in Pittsburgh, PA, USA, 2013. The ‘full-service supermarkets’ category includes only full-service supermarkets; ‘mass merchandising/discount grocery stores’ include supercentres, wholesale clubs and discount grocery stores; ‘convenience stores’ include dollar stores, drug stores, convenience stores and neighbourhood stores; ‘other’ stores include meat or seafood markets, specialty grocery stores and all other types of food stores. The ‘other restaurants’ category includes buffet or cafeteria; bar, tavern or lounge; coffee shop; and other type of restaurant. Percentages of households that returned at least one receipt from each type of store and restaurant were calculated with the total number of households as the denominator (N 293). Percentages of total expenditures by store and restaurant type were calculated with the total food and beverage expenditures in all food retail outlets (stores and restaurants) across all households in the sample as the denominator ($US 18 398·10) 2612 CA Vaughan et al. foods and beverages in different types of stores (see Table 3). For unhealthy foods, we focused on salty snacks, sweets and sugar-sweetened beverages, all of which consist primarily of empty calories; for healthy foods, we focused on fruits and vegetables. In general, purchases of both unhealthy and healthy foods were more common in full-service supermarkets than in other stores. For example, 109 households had purchased sweets in full-service supermarkets, whereas purchases of sweets in convenience stores were made by roughly two-thirds as many households (n 76) and purchases of sweets in mass merchandisers/discount grocery stores were made by roughly half as many households (n 56); very few households purchased sweets Table 2 In-store food and beverage expenditures of households in a food desert in Pittsburgh, PA, USA, 2013 % of total Average expenditures, all households (N 293)† ($US) Average expenditures of households with at least one food or beverage item purchase‡ ($US) Households with at least one food or beverage item purchase Food/beverage category expenditures* Mean SD Mean SD n % Food 87·9 51·07 60·74 56·25 61·51 266 90·8 Protein 38·0 19·41 28·41 26·21 30·20 217 74·1 Salty snacks 5·1 2·59 4·08 5·31 4·43 143 48·8 Sweets 11·3 5·76 8·77 8·70 9·53 194 66·2 Other empty calories§ 6·1 3·11 5·86 6·64 7·07 137 46·8 Grains 9·2 4·69 5·90 6·95 5·99 198 67·6 Vegetables 8·6 4·37 7·46 8·42 8·56 152 51·9 Fruit 7·2 3·69 5·90 6·76 6·57 160 54·6 Prepackaged/takeaway entrées 6·9 3·53 7·28 9·77 9·26 106 36·2 Dairy 3·9 2·01 4·04 5·73 5·02 103 35·2 Other 3·7 1·89 6·52 7·21 11·15 78 26·6 Beverages 12·1 6·99 9·65 9·25 10·12 222 75·8 Sugar-sweetened beverages 40·2 2·81 4·19 5·20 4·47 159 54·3 Milk 16·8 1·18 2·16 3·48 2·40 99 33·8 Fruit/vegetable juice 11·1 0·78 2·29 4·28 3·77 53 18·1 Water 7·4 0·52 1·75 4·00 3·12 38 13·0 Coffee/tea 7·2 0·51 2·27 4·49 5·36 34 11·6 Alcohol 6·5 0·46 4·48 33·48 21·97 4 1·4 Artificially sweetened and low-calorie 5·6 0·38 1·49 3·56 3·07 32 10·9 Condiment (e.g. creamer) 3·1 0·21 0·86 3·29 1·12 19 6·5 Specialty drinks (e.g. latté) 2·2 0·15 1·62 7·35 9·43 6 2·1 *Percentages for both of the overarching food and beverage categories of expenditures were calculated with the total food and beverage expenditures ($US 17 015·48) as the denominator. Percentages for sub-categories of food and beverage expenditures were calculated with the total food ($US 14 962·71) and beverage ($US 2052·77) expenditures, respectively, as the denominators. †The numbers in the ‘average expenditures, all households’ columns are the average (mean) amount of money spent on each type of food or beverage across all households during the 7 d period of data collection (N 293) and the SD. ‡The numbers in the ‘average expenditures of households with at least one food or beverage item purchase’ columns are the average (mean) amount of money spent on each type of food or beverage by households that purchased at least one food or beverage item in the corresponding category and the SD; for example, the average shown for protein is the average amount of money spent on protein by households that bought at least one protein item during the 7 d period of data collection and the SD. The number and percentage of households that purchased at least one food or beverage item in the corresponding category are also shown. §Other empty calories include butter, margarine, shortening, condiments, dips and gravy. Table 3 Average household expenditures ($US) on each type of food and sugar-sweetened beverages (SSB) by store type among the subset of households that made at least one purchase of the particular type of food or SSB in each type of store, Pittsburgh, PA, USA, 2013 Unhealthy foods and beverages Healthy foods Salty snacks Sweets SSB Fruits Vegetables Store types n* Mean† SD n Mean SD n Mean SD n Mean SD n Mean SD Full-service supermarkets 67 5·24 4·75 109 8·32 8·22 97 5·46 4·27 113 6·15 5·91 115 7·99 8·19 Mass merchandisers/discount grocery stores 41 4·19 2·53 56 8·36 10·53 31 3·82 2·90 50 6·15 6·56 49 5·83 4·94 Convenience stores 52 3·83 3·27 76 3·29 2·74 56 2·98 2·33 16 2·24 1·39 5 1·40 0·49 Other 10 3·72 2·25 7 9·03 12·99 6 1·96 1·71 7 6·21 7·83 7 9·86 12·40 *The numbers shown in all of the …