Social Welfare Issue Policy

R E V I E W

Supplemental Nutrition Assistance Program

(SNAP): does receiving assistance impact food

choices? This article was published in the following Dove Press journal:

Nutrition and Dietary Supplements

Prageet K Sachdev

Mahsa Babaei

Jeanne H Freeland-Graves

Department of Nutritional Sciences,

College of Natural Sciences, The

University of Texas at Austin, Austin, TX

78712, USA

Abstract: The Supplemental Nutrition Assistance Program (SNAP) is the largest federal

program that provides assistance for the purchase of foods to low-income households in the

United States. SNAP plays a valuable role in alleviating hunger and food insecurity in poor

households; however, one consideration that remains relatively unexplored is the influence of

this program on food choices. Food choices are guided by several factors in low-income

individuals, including the cost of food, household size, nutrition knowledge, availability of

fresh foods in the neighborhood, transportation, and cultural factors. Also, the complex

relationship between SNAP participation and food choices is further confounded by the

factors of demographics, food insecurity, poverty, and self-selection. There is a lack of

quantitative investigations that directly evaluate food choices in SNAP recipients. As a

result, this review will focus on summarizing finding from studies that assessed food

purchasing patterns, diet quality, and weight gain in SNAP participants. These outcomes

may serve as proxy measures to evaluate the food choices made by SNAP participants. In

addition, this review discusses many behavioral economic strategies such as reducing the

cost of healthy foods, providing monetary benefits for purchase of healthy foods, increasing

the SNAP benefits, incentivizing small food retailers to offer more food choices in low-

income neighborhoods, increasing grocery stores and supermarkets in poor neighborhood,

and strengthening the SNAP-Ed program; some of which have been previously adopted to

promote the selection of healthy foods in SNAP participants. SNAP has the potential to

impact food choices in the society, as such longitudinal studies are needed to evaluate the

effectiveness of any reforms in SNAP benefits or restrictions, which may seem logical but

not impact food choices in reality.

Keywords: Supplemental Nutrition Assistance Program, food choices, grocery purchase,

diet, diet quality, obesity

Introduction The Supplemental Nutrition Assistance Program (SNAP) is the largest federal

program that provides assistance for the purchase of foods to low-income house-

holds in the United States (U.S.).1 It provides food purchasing benefits to house-

holds with an annual income level of ≤130% of the Federal Poverty Level (FPL) and household assets of <$2000.2 The original name was the Food Stamps program

which was established in 1964. The renaming occurred in 2008 in order to increase

its focus to improve nutrition in beneficiaries.2 In the fiscal year 2018, SNAP

benefits totaled $60.6 billion, which were provided to 40.3 million Americans.

Correspondence: Jeanne H Freeland-Graves Department of Nutritional Sciences, College of Natural Sciences, The University of Texas at Austin, T.S Painter Hall 5.20, 103 W 24TH ST A2703, Austin, TX 78712, USA Tel +1 512 471 0657 Fax +1 512 471 5844 Email [email protected]

Nutrition and Dietary Supplements Dovepress open access to scientific and medical research

Open Access Full Text Article

submit your manuscript | www.dovepress.com Nutrition and Dietary Supplements 2019:11 19–35 19 DovePress © 2019 Sachdev et al. This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.

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http://doi.org/10.2147/NDS.S177809

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The average benefit per person per month was $125.63,

with two-thirds of all SNAP participants being children,

elderly or those with a disability; the majority live below

the FPL.3 This program is based on United States

Department of Agriculture’s (USDA) Thrifty Food Plan

(TFP), a low-cost plan which suggests monthly food

expenses derived from the consumer price index.4 The

funds are distributed monthly on an electronic benefits

transfer card, which can be used to purchase most foods

and beverages, except dietary supplements, alcohol,

tobacco, and ready to eat foods.5

The SNAP plays a major role in reducing hunger and

food insecurity in low-income Americans.6 It is designed

such that the maximum benefits (92%) are received by

households with incomes at or below the poverty line,

while 56% go to households at or below half of the poverty

line (about $10,390 for a family of three in 2018).6 This

program acts as a safety net for the elderly, those with

disability, temporarily unemployed, and low-income wage

workers.6 In addition, it provides adequate nutrition support

to low-income groups by strengthening their power to pur-

chase foods. Finally, SNAP nutrition education programs

help to improve the food choices made by the recipients.6

However, food choices are guided by numerous factors

in low-income individuals, including the cost of food,7

household size,8 nutrition knowledge,9 transportation,10,

and cultural factors.11,12 A focus group study analyzed

the food choices among food stamp participants.8 The

conclusion was that the cost of the food was the major

consideration in deciding which items were purchased.

Family size was an additional influence, with those having

larger families preferring inexpensive foods in bulk in

order to satisfy the needs of everyone.8 Lack of nutrition

knowledge, problems in understanding food labels and less

support from the family for healthy recipes were few other

barriers reported for eating healthy.8 Lack of personal

transportation restricts grocery expenditures to the nearest

convenience store.13 Yet, an analysis of the National

Household Food Acquisition and Purchase Survey dataset

study found that a constraint in transportation did not

result in difference in types of stores, as compared to

their non-participating counterparts.14 Culture is yet

another significant factor in determining food choices.

This is especially true in Hispanic and African-American

households, as most preferred preparing traditional

recipes.8 Very few individuals declared that their food

choices were influenced by the media, nutrition knowl-

edge, and/or physician advice.8

SNAP provides food assistance to the vulnerable and

low-income populations. However, there is limited evi-

dence from quantitative studies that directly assess the

effect of participation in SNAP on food choices.

Therefore, the goal of this review is to analyze the existing

literature on outcomes of food expenditure patterns, diet

quality, and obesity in SNAP participants, with the pur-

pose of studying food choices from these proxy measures.

Potential impact of SNAP on food choices About one in eight Americans participate in SNAP each

month.15 It serves as a valuable program to reduce food

insecurity in numerous high-risk segments of the popula-

tion. However, recent adequacy of SNAP benefits has been

debated. Reasons include geographical variations in the

cost of foods,16 problems associated with the TFP which

forms the basis of the SNAP4 and frequency of distribution

of the monthly benefits.

Geographical variations in the cost of food

SNAP benefits are fixed across the 48 states (higher in

Alaska and Hawaii); however, variations in pricing of the

food items occur in different areas of the country. At

present, SNAP benefits are not adjusted to regional differ-

ences in food prices, as it would be politically untenable.

Thrifty food plan

A problem with the TFP being the basis for the SNAP

benefits is that it is focused primarily on food items that

are raw or require significant time for preparation.4 The

underlying assumption is that individuals have sufficient

time and skills for preparation, accessibility, and afford-

ability to all food items. Furthermore, the TFP offers only

a limited variety of foods, hampering its ability to meet the

Dietary Guidelines.4

Frequency of distribution

SNAP benefits are distributed at the beginning of every

month. However, the majority of grocery shopping takes

place within the first 3 days of receipt of benefits.17,18 This

distribution leads to a pattern known as “food stamp

cycle” in which participants use their SNAP benefits

within the first 2 weeks of receipt.19 Research shows that

this infrequent distribution of benefits may have a negative

influence on participants’ nutritional status. It has been

shown that some food stamp recipients have cyclical pat-

terns of food consumption, characterized by periods of

overconsumption during the first part of the month after

receiving benefits when financial resources and food are

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more abundant.20,21 This is followed by a period of under-

consumption at the end of the benefit cycle when the

quantity and quality of foods being consumed are reduced

due to the depletion of benefits.20,21

All the above factors may influence the choices for

foods selected by SNAP participants. In the absence of

studies that directly assess food choices, this review aims

to collect, analyze, and summarize the evidence on food

choices using outcomes of patterns of food expenditures,

nutrient intake and diet quality, and weight gain in the

SNAP participants.

Design Figure 1 illustrates the process of literature review and

study selection. A search of the databases of PubMed,

Web of Science, Embase, and Cochrane Library was con-

ducted to identify prospective, case-controlled, and cross-

sectional studies investigating food purchasing patterns,

food expenditures, food choices, nutrition, and diet quality

assessment in SNAP participants from January 1963 to

December 2018. Keywords chosen were Supplemental

Nutrition Assistance Program or SNAP, low-income

households, adequacy of the SNAP, variations in SNAP

benefits, food purchasing patterns, food expenditure, diet

quality, diet analysis, and nutrient analysis.

Inclusion and exclusion criteria Inclusion criteria were prospective, cross-sectional, or

case–control investigations that measured food expendi-

tures and diet or diet quality of SNAP and income-eligible

non-SNAP individuals. Exclusion criteria were the

absence of full text and not being available in English.

Data extraction A total of 124 studies were retrieved using the keywords

listed above. Two investigators separately examined the

studies and the results were compared. Duplicate experi-

ments were removed, and any disagreement regarding

inclusion was resolved with the help of a third researcher.

Initially, 65 studies were retrieved, which were then sub-

ject to the exclusion criteria, this resulted in a total of 51

studies included in the final analysis. The main findings of

the studies were divided into three topics for assessing

evidence for food choices in SNAP participants: investiga-

tions on food expenditure; nutrient intake and diet quality;

and obesity and body mass index (BMI).

124 studies identified from pub med, embase, cochrane library and web of science; January 1963-December 2018 key words: supplemental nutrition assistance program or SNAP, low-income households, food purchasing patterns, food expenditure, diet quality, diet analysis, nutrient analysis.

Full text not available = 5

Duplicate studies = 14

51 studies selected for final analysis

119 studies selected for closed review

65 studies retrieved inclusion criteria: prospective, cross-sectional, or case-control investigation measured food expenditure, diet and diet quality of SNAP and income-eligible non-SNAP individuals.

Figure 1 Flowchart demonstrating the process of study selection for systematic review on the food purchasing patterns and diet quality of Supplemental Nutrition Assistance Program (SNAP) and non-SNAP participants.

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Quality of evidence This systematic review used the GRADE (Grading of

Recommendations Assessment, Development, and

Evaluation Working Group) to evaluate the quality of

evidence for the three different outcomes considered:

food expenditures, diet quality, and BMI/obesity in

SNAP participants.22 The GRADE uses a criterion based

on an assessment of four factors for each outcome: risk of

bias, consistency, directness, and precision.22 Table 3 illus-

trates the process of grading the evidence for the three

outcomes based on these four criteria. In this review,

randomized-controlled trials were rated as high evidence

and observational studies as low. The grade for the out-

come was reduced if there were any inconsistencies in the

results of the studies or the methods for assessment of the

outcomes, uncertainty about directness, small data sizes,

reporting bias or serious limitations in the studies.23 The

grade was increased if there was a strong or very strong

evidence of association (p<0.05) based on evidence from

observational studies, with adjustment for confounders,

with the satisfaction of internal validity.23 Based on the

above criteria, the quality of evidence was rated as high,

moderate, low, and very low.22

Results Food choices influenced by the

inadequacies of the SNAP Leibtag et al, showed that food prices in the West and

Northeast are above average, while those in the South and

Midwest are below the mean.16 This uneven distribution of

food costs suggests that clients in the South and Midwest

can purchase more healthy food items, as compared to those

in the West and Northeast.16 Bronchetti et al, utilized the

1999–2010 data from the National Health Interview Survey

and found that in areas with lower food prices, higher real

SNAP purchasing power was linked to a lower probability

of being food insecure in children, ages 17 and under.24

The TFP is used to calculate the benefits for the SNAP

clients. Research at the Tulane University concluded that

the recipients would need to spend 2 hrs daily for prepar-

ing meals in order to follow the TFP.4 In single adult

households with children, some are constrained by time

for food preparation. These households spend 142% of the

TFP cost on food, as compared to two adult households

that spend 119% of the TFP cost. Thus, SNAP benefits

may be insufficient to meet the needs of single-parent

families.4

Few studies have investigated the variations in the

utilization of the benefits over the period of a month.

Hastings et al, reported that food expenditures, relative to

non-benefit recipients, fall by 30% in week 4 after benefit

distribution as compared to week 1.25 This uneven disse-

mination of funds leads to a decline of benefits as the

month progresses, resulting in a deterioration of nutrient

intake and diet quality over time.26 Hamrick et al, found

that the likelihood of not eating in a day increased toward

the end of the month, when the benefits are exhausted.27

Sanjeevi et al, observed a significant decrease in the con-

sumption of fruits, vegetables, and diet quality of SNAP

women participants toward the end of the monthly benefit

period.26 In 244 African-American SNAP participants, a

decrease in the diet quality occurred over time since SNAP

distribution.28 Thus, these studies provide evidence of a

decline in diet quality from the time of receipt of benefits.

Food choices as illustrated by food

expenditure studies The food purchasing patterns of SNAP households are

described in Table 1. Previous studies have utilized gro-

cery receipts and other food acquisition data to investigate

the food purchasing patterns of SNAP households. In a

cross-sectional study of 4826 households, Tiehen et al,

documented lower expenditure of food in SNAP house-

holds, as compared to non-SNAP households, after adjust-

ing for the household size and composition. Also, SNAP

households exhibited the highest expenditure on food

items just after receipt of the benefits.29

In a recent focus group study by Moran et al, SNAP

participants reported that they purchased ultra-processed

foods because these items have a long shelf life and could

be stored to prevent food shortage at the end of the month.30

Franckle et al, analyzed the sales data over a period of two

years and found that the SNAP participants spent greater on

sugary beverages, red meat, and convenience foods and less

on vegetables, fruits, and poultry as compared to

nonparticipants.31 In 2017, Gustafson et al, reported that

SNAP households purchased more (62%) sugar-sweetened

beverages (SSBs) (41%) and less milk (60%) compared to

non-SNAP participants.32 Similar results were shown by two

other studies by Grummon et al, and Andreyeva et al, who

documented that SNAP benefits were used to purchase foods

higher in saturated fats and sodium33 and SSB, respectively.34

In contrast, a 2011 study by the USDA collected point

of sale data from grocery stores, supermarkets, and drug

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stores. For every $1, both SNAP and non-SNAP households

spent 40 cents on basic items of vegetables, milk, eggs,

bread; 20 cents on sweetened beverages, desserts, and salty

snacks; and 40 cents on cereals, rice, and beans.35 The

conclusion was that SNAP and non-SNAP households

spent a similar amount of money on sweetened beverages,

salty snacks, and prepared beverages.35 In another study by

Sanjeevi et al, higher expenditures were reported for red

meat, refined grains, whole fruits, and other vegetables in

SNAP households.36 In addition, the household percentage

expenditures for dark green and orange vegetables, and

whole grains were significantly lower than the TFP

recommendations.36

Most of the research to date has conducted secondary

analysis of national datasets to predict variations in food

choices as SNAP benefits change. Anderson et al, pre-

dicted an increase in expenditure on groceries by $19.48,

with a raise of $30 per capita in monthly SNAP benefits.37

Table 1 Characteristics of studies included in the systematic review of food purchasing patterns in the Supplemental Nutrition Assistance Program (SNAP) and non-SNAP participants

Study, Reference Participants Methods Findings

Moran et al, 2019 30

45 SNAP and non-SNAP

households

Focus group Some SNAP clients purchased ultra-processed

foods because of longer shelf life; storage

would prevent problem of food shortage at the

end of the month

Sanjeevi et al, 2018 36

160 SNAP women

participants

Cross-sectional analysis of gro-

cery receipts

Highest expenditures were made on refined

grains, red meat, whole fruits, and other vege-

tables. Lowest expenditures were soups,

orange vegetables, whole grain breads, rice, and

pasta.

Tiehen et al, 2017 29

4826 SNAP and non-SNAP

households

Cross-sectional analysis of food

acquisition and purchase survey

data

Spending on food was lower in SNAP house-

holds than income-eligible non-SNAP, after

adjusting for household size and composition.

Daily food expenditures were higher on days

just after benefits were received.

Gustafson et al, 2017 32

1581 SNAP and 1382 non-

SNAP households

Cross-sectional analysis of Food

Acquisition and Purchase Survey

data

SNAP households purchased more sugar-

sweetened beverages than non-SNAP house-

holds (62% vs 41%) and less milk (54% vs 60%)

Franckle et al, 2017 31

188 stores across five states

(Maine, Massachusetts,

Vermont, New Hampshire,

New York)

Sales data from a chain supermar-

ket in Northeastern US over 2

years

SNAP clients spent less on fruits, vegetables,

and poultry and more on sugar-sweetened

beverages (SSB’s), red meat, and convenience

foods than nonparticipants

Grummon et al, 2017 33

98,256 SNAP and non-SNAP

households

Cross-sectional analysis of 2012–

2013 packaged food and beverage

purchases by SNAP participants.

SNAP households spent less on fruits, vegeta-

bles and fiber, but more on sugar-sweetened

beverages, salty snacks, sweeteners, and pro-

cessed meat than nonparticipants

Garasky e al, 2016 35

26.5 million SNAP and non-

SNAP households

Cross-sectional analysis of point

of sale transaction data

No major differences in expenditures between

SNAP and non-SNAP households. Proteins

were major food product purchased

Anderson et al, 2016 37

SNAP clients Cross-sectional study Analysis of

2001–2014 Food Security

Supplements of the current popu-

lation surveys

An increase in SNAP benefits by $1 would raise

food spending by 68¢ per capita; $30 would

increase expenditure by $19.48

Andreyeva et al, 2012 34

39,172 SNAP households Cross-sectional analysis of gro-

cery store scanner data from a

regional supermarket chain

72% of sugar-sweetened beverages were pur-

chased using SNAP benefits. Expenditure on SSB’s

was higher than those on all groceries (63%), diet

(65%), and unsweetened beverages (59%)

Bradbard et al, 1997 8

28 focus groups of SNAP

participants

Focus group Food purchased at beginning of month are

those that can be stored for later consumption

(canned vegetables and grains)

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It was further found that this increase in spending was

related to higher consumption of healthier foods and lower

food insecurity in the participants.37

Economic Research Service estimated the demand

elasticities using the 1987–88 Nationwide Food

Consumption Survey; it was concluded that a decline of

20% in food price would raise fruit and vegetable con-

sumption by 2.2 cups in SNAP recipients.38 Yet, the

Consumer Expenditure Survey (CE) of the Bureau of

Labor Statistics data of 1991 and 2000 predicted no

increase in purchase of fruits and vegetables as SNAP

benefits escalated by $1.39

Finally, Castner et al, used data from the 1996 National

Food Stamp Program Survey (NFSPS) and 2001–2004

surveys to investigate associations between spending on

food and diet quality using the HEI-2005.40 A 10%

increase in spending on food was positively associated

with a rise in a household’s HEI-2005 score of 0.33%

via NFSPS data, and an increase of 0.30% via NHANES

data.40 With both datasets, improvements in diet quality

were higher with an increase in benefits for fruit and

vegetables.

Food choices as illustrated by nutrient

intake and diet quality studies The SNAP has played a major role in reducing hunger and

food insecurity in the US. Table 2 illustrates the effect of

participation in SNAP on the diet quality of individuals.

Diet quality refers to both the quality and variety of the diet

as measured by assessing the extent of alignment of food

patterns with dietary guidelines.41 The results from studies

investigating diet quality in SNAP recipients have been

inconsistent, with some investigations observing none to

negative42–44 impact on diet quality. For example, Lacko

et al, documented comparable consumption of calories from

fast-foods, and lower consumption of whole fruits and

whole grain in both participants and nonparticipants.45

Yet, Zhang et al, analyzed NHANES data of 38,696 adults

from 1999 to 2014 and found lower diet quality scores of

SNAP participants as compared to the nonparticipants over

the years.43 Furthermore, results from a secondary analysis

of NHANES data from 1999–201246 and 2007–201044

showed that SNAP participants had lower diet quality as

compared to income-eligible nonparticipants. Taille et al,

documented that SNAP households consumed greater

energy from SSBs, desserts, processed meats as compared

to their counterparts.47 A systematic review documented

that adult SNAP participants had poor diet quality as com-

pared to the nonparticipants. However, intake of total kilo-

calories, macro and micronutrients did not differ

significantly between participants and income-eligible

nonparticipants.48 Finally, Nguyen et al, found that SNAP

participation improved diet quality in the food insecure

groups.49

In the analyses of a national dataset, participation in

SNAP was associated with an increased probability of

consumption of whole fruits by 23%.50 This increase

may be because of the extra income through SNAP and

the convenience factor associated with eating fruits that

require no preparation time.50 Similar consumption of

fruits and vegetable has been documented in both partici-

pants and nonparticipants.42,51,52 In contrast, consumption

of dark green/orange vegetables was found to be low in

SNAP participants.50

The evidence on the consumption of SSBs in SNAP vs

the nonparticipants is mixed; with few reporting higher

consumption42,53,54 to no differences.55,56 However, two of

the investigations showing higher consumption in SNAP

participants analyzed regional data sets34,57 and one did

not report any difference for men.42 The evidence of

SNAP on other food choices such as whole grains is also

mixed. A study by Caster et al, found that SNAP recipients

used more whole grains and grains that were more nutrient

dense, as compared to income-eligible participants.40 In

contrast, in a secondary analysis of the NHANES data

involving 3142 women, Jun et al, reported no differences

in whole grain intake between low-income and high-

income women groups. The Hilmers et al, study on low-

income women, consumption of whole grains was found

to be lower both in participants and non-SNAP partici-

pants relative to the dietary guidelines.58 Few other studies

have documented similar consumption of total grains

among both groups.51,56

Food choices from weight and BMI studies Previous research has documented higher weight gain and

obesity in adult women SNAP participants20,59–61 and

female children,62,63 but a decrease in obesity for male

children.62 Analysis of the data from 2003 to 2006

NHANES showed that the SNAP participation was

directly related to obesity [prevalence ratio: 1.58] in both

adult men and women participants.64 In Los Angeles,

SNAP participants were found to have twice the higher

odds of obesity, as compared to nonparticipants.65 In con-

trast, analysis of the NHANES data from an earlier period,

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1999–2002, did not find any differences in BMI between

SNAP participants and nonparticipants.66 Zagarosky et al

found that BMI of women SNAP participants was greater

than one unit higher as compared to nonparticipants with

similar socio-economic characteristics.67 Furthermore, the

length of participation in SNAP showed a cumulative

effect on BMI; longer periods were related to greater

increases in BMI.67 In contrast, Fan et al, analyzed the

National Longitudinal Study of Youth data, and found no

association between participation in SNAP and obesity in

adult women.68 Three longitudinal studies found that par-

ticipation in SNAP over a long period of time was asso-

ciated with a higher BMI in young girls, young daughters

of mothers who were obese, and children living in

cities.63,69,70

The results from studies in men are mixed. Results

from studies, six59,61,67,70–72 found no association between

participation in SNAP and risk of obesity, however, two

Table 2 Characteristics of the studies included in the systematic review on diet quality in the Supplemental Nutrition Assistance Program (SNAP) and non-SNAP participants

Study, Reference Participants Methods Findings

Lacko et al, 2018 45

2523 SNAP participants

and nonparticipants

Cross-sectional study analysis

of NHANES 2011–2014

SNAP clients consumed greater amounts of solid fats

and added sugar from at home foods than non-SNAP

participants. Both groups consumed low quantities of

non-starchy vegetables, whole fruits, and whole grains

Zhang et al, 2018 43

38,696 SNAP partici-

pants and

nonparticipants

Cross-sectional study analysis

of NHANES 1999–2014

From 2003 to 2014, there was less improvement in diet

quality scores of SNAP participants (change in average

score=0.57) as compared to both income-eligible non-

participants (2.56) and higher-income individuals (3.84)

Taillie et al, 2018 47

76,458 SNAP and non-

SNAP households

Cross-sectional study nutrition

profile of purchases by store

type 2010–2014

Calories obtained from processed meat, starchy vege-

tables, sweeteners, desserts, toppings, total junk food,

sugar-sweetened beverages, and milk were higher

among SNAP participants as than income-eligible and

higher-income nonparticipants

Gu et al, 2017 46

38,487 Children and

adolescents

Cross-sectional study analysis

of NHANES 1999–2012

After the 2003–2004 cycle, SNAP clients scored lower

on the Healthy Eating Index-2010 than nonclients

Condon et al, 2015 44

SNAP participants and

nonparticipants

Cross-sectional study analysis

of NHANES 2007–2010

SNAP clients consumed more energy from solid fats

and added sugars; and scored lower on the HEI-2005

than both income-eligible and higher-income

nonparticipants

Andreyeva et al, 2015 48

25 studies SNAP partici-

pants and

nonparticipants

Systemic review Adult SNAP participants had lower scores on the

Healthy Eating Index than SNAP-eligible nonparticipants

and SNAP-ineligible nonparticipants. Diets for children

were similar for both groups

Nguyen et al, 2015 49

SNAP participants and

nonparticipants

Analysis of NHANES

2003–2010

Participation in SNAP was associated with better diet

quality in marginal, low, and very low food security

groups

Nguyen et al, 2015 54

SNAP participants and

nonparticipants

Analysis of NHANES

2003–2010

Total kilocalorie intake from sugar-sweetened beverages

was higher (12%) than income-eligible nonparticipants

(9%) and those who were ineligible to participate (6%)

Bleich et al, 2013 55

SNAP participants and

nonparticipants

Examination of patterns of

adult consumption of SSBs by

SNAP eligibility 2003–2010

Percentage consumption of sugar-sweetened beverages

and per capita calorie intake from SSBs were higher in

individuals receiving SNAP than those not eligible to

participate in SNAP

Leung et al, 2013 52

Children with household

incomes of <130% of

Federal Poverty Level

Cross-sectional analysis of

NHANES a 1999–2008

Diets of SNAP clients had 44% more servings of pro-

cessed meats, 43% more servings of sugar-sweetened

beverages and 47% more servings of high-fat dairy than

nonclients. No significant differences were observed for

whole grains, fruits, and vegetables between group

Note: aNational Health and Nutrition Examination Survey.

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DovePress 25

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T a b le

3 G R A D E (G

ra d in g o f R e c o m m e n d a ti o n s A ss e ss m e n t, D e v e lo p m e n t, a n d E v a lu a ti o n W o rk in g G ro u p ) to

e v a lu a te

th e q u a li ty

o f e v id e n c e fo r th e d if fe re n t o u tc o m e s u se d to

a ss e ss

fo o d c h o ic e s

O u tc o m e

m e a su

re d ,

st u d ie s (n )

D e si g n , st u d ie s

(n )

R is k o f b ia s

C o n si st e n cy

D ir e ct n e ss

P re ci si o n

Q u a li ty

o f

e vi d e n ce

F o o d e x p e n -

d it u re

(1 0 )

F o c u s g ro u p (2 )

P a rt ic ip a n ts

m ay

n o t c h o o se

to e x p re ss

th e ir

th o u g h ts fr o m

fe a r o f ju d g m e n t, se lf -s e le c ti o n o f

p a rt ic ip a n ts

in th e fo c u s g ro u p s, in c lu d e d o n ly

S u p p le m e n ta l N u tr it io n A ss is ta n c e P ro g ra m

(S N A P ) p a rt ic ip a n ts , d is c u ss io n m a in ly st e e re d

b y th e m o d e ra to r

C o n si st e n t d if fe re n c e s

o b se r v e d in

fo o d

e x p e n d it u re

p a tt e rn s

b e tw

e e n S N A P a n d

n o n p a rt ic ip a n ts

L o w ; in fo rm

a ti o n

p ro v id e d b y

p a r ti c ip a n ts

V e r y lo w

p re c is io n

V e r y lo w

C ro ss -s e c ti o n al (7 )

S e lf -s e le c ti o n in

S N A P, p a rt ic ip a n ts

m ay

d if fe r in

c e rt a in

c h a ra c te ri st ic s, n o e v id e n c e o f a te m -

p o ra l re la ti o n sh ip

b e tw

e e n e x p o su re

a n d o u t-

c o m e , se lf -c o m p le te d q u e st io n n a ir e s

C o n si st e n t fi n d in g s

M o d e ra te ; u se

o f

p o in t o f sa le

d a ta ,

g ro c e r y re c e ip ts

L o w

to m o d e ra te ; sa m p le

si z e s w e re

> 1 0 0 0 ,

o d d s ra ti o fo r st u d ie s v a ri e d b e tw

e e n 0 .8 4 a n d

2 .5 0 , n a rr o w

c o n fi d e n c e in te r v a ls , m o st

o f th e

st u d ie s a d ju st e d fo r h o u se h o ld

a n d n e ig h b o r-

h o o d c h a ra c te ri st ic s

M o d e ra te

D ie t q u a li ty

in a d u lt s (1 5 )

C ro ss -s e c ti o n a l,

u se d c o n v e n ie n c e

sa m p le s (4 )

n a ti o n a ll y re p re -

se n ta ti v e d a ta

(1 1 )

S e lf -s e le c ti o n in

S N A P, p a rt ic ip a n ts

m ay

d if fe r in

c e rt a in

c h a ra c te ri st ic s, re c a ll b ia s, c o n v e n ie n c e

sa m p le

m ay

o r m ay

n o t b e re p re se n ta ti v e o f

p o p u la ti o n st u d ie s, n o e v id e n c e o f a te m p o ra l

re la ti o n sh ip

b e tw

e e n e x p o su re

a n d o u tc o m e ,

se lf -r e p o rt e d d a ta

M ix e d re su lt s o n d ie t

q u a li ty

a c ro ss

st u d ie s

L o w

to m o d e r-

a te ; d if fe re n t

to o ls u se d to

c a p tu re

d ie ta r y

in ta k e a n d d ie t

q u a li ty

L o w

to m o d e ra te

p re c is io n , m o st

st u d ie s u se d

la rg e n a ti o n a l sa m p le s (n > 1 0 0 0 ), re c o rd e d si g -

n ifi c a n t d if fe re n c e s (p < 0 .0 0 0 1 ), a d ju st e d fo r

c o n fo u n d in g re la te d to

so c io d e m o g ra p h ic fa c -

to rs . T h re e st u d ie s u se d ri g o ro u s st a ti st ic a l

te c h n iq u e s to

st u d y c a u sa li ty

L o w -m

o d e ra te

D ie t q u al it y

in c h ild re n (6 )

C ro ss -s e c ti o n a l,

u se d n a ti o n a ll y

re p re se n ta ti v e

d a ta

(6 )

S e lf -s e le c ti o n , se lf -r e p o rt e d d a ta , re c a ll b ia s, n o

e v id e n c e o f a te m p o ra l re la ti o n sh ip

b e tw

e e n

e x p o su re

a n d o u tc o m e

C o n si st e n t re su lt s o n

d ie t q u a li ty

L o w

to m o d e r-

a te ; d if fe re n t

to o ls u se d to

c a p tu re

d ie ta r y

in ta k e a n d d ie t

q u a li ty

M o d e ra te

p re c is io n , m o st

st u d ie s u se d la rg e

n a ti o n a l sa m p le s (n > 1 0 0 0 ), sm

a ll st a n d a rd

e rr o rs

a ss o c ia te d w it h e st im a te s. M o st

o f th e

st u d ie s a d ju st e d fo r c o n fo u n d in g re la te d to

so c io d e m o g ra p h ic fa c to rs

M o d e ra te

D ie t q u a li ty

in h o u se -

h o ld s (6 )

C ro ss -s e c ti o n a l,

u se d c o n v e n ie n c e

(1 ) n a ti o n a ll y

re p re se n ta ti v e

d a ta

(5 )

S e lf -s e le c ti o n , se lf -r e p o rt e d d a ta , re c a ll b ia s,

c o n v e n ie n c e sa m p le

m ay

o r m ay

n o t b e re p re -

se n ta ti v e o f p o p u la ti o n st u d ie s, n o e v id e n c e o f a

te m p o ra l re la ti o n sh ip

b e tw

e e n e x p o su re

a n d

o u tc o m e . S tu d ie s d id

n o t a c c o u n t fo r d ie ta r y

d if fe re n c e s a t in d iv id u a l le v e l.

M ix e d re su lt s

L o w

to m o d e ra te

L o w

p re c is io n , m o st

la rg e n a ti o n a l sa m p le s

(n > 1 0 0 0 ) re c o rd e d n o to

so m e d if fe re n c e s

(p < 0 .0 0 0 1 ), h o w e v e r, h o u se h o ld

d a ta

d o e s n o t

a c c o u n t fo r in d iv id u a l d if fe re n c e s in

d ie t.

L o w ( C on tin ue d)

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T a b le

3 (C

o n ti n u e d ).

O u tc o m e

m e a su

re d ,

st u d ie s (n )

D e si g n , st u d ie s

(n )

R is k o f b ia s

C o n si st e n cy

D ir e ct n e ss

P re ci si o n

Q u a li ty

o f

e vi d e n ce

O b e si ty

in

m a le s (8 )

C ro ss -s e c ti o n al (4 )

S e lf -s e le c ti o n in

S N A P, n o e v id e n c e o f a te m -

p o ra l re la ti o n sh ip

b e tw

e e n e x p o su re

a n d o u t-

c o m e , st u d ie s d id

n o t a c c o u n t fo r th e d u ra ti o n

o f p a rt ic ip a ti o n in

p ro g ra m

a n d b e n e fi ts

re c e iv e d

M ix e d re su lt s

L o w

to m o d e r-

a te ; B M I a n d

w a is t c ir c u m fe r-

e n c e u se d to

m e a su re

w e ig h t

L o w

p re c is io n , n o a d ju st m e n t fo r c o n fo u n d in g

e ff e c t o f ti m e , sa m p le

si z e s w e re

m o d e ra te

to

la rg e , e st im a te s v a ri e d b e tw

e e n st u d ie s, w it h

si g n ifi c a n t to

in si g n ifi c a n t p- v a lu e s

L o w

L o n g it u d in a l (4 )

S e lf -s e le c ti o n in

S N A P, lo ss

o f p a rt ic ip a n ts

w a s

m o d e ra te

to su b st a n ti a l, c o u ld a ff e c t th e v a li d it y

o f re su lt s, lo n g it u d in a l d e si g n a ll o w e d to

o b se r v e e ff e c t o f ti m e o n w e ig h t g a in

M ix e d re su lt s

M o d e ra te ; B M I

u se d to

m e a su re

w e ig h t

L o w

to m o d e ra te

p re c is io n , a d ju st e d fo r ti m e .

L o w -m

o d e ra te

O b e si ty

fe m a le s (1 1 )

C ro ss -s e c ti o n al (5 )

S e lf -s e le c ti o n in

S N A P, n o e v id e n c e o f a te m -

p o ra l re la ti o n sh ip

b e tw

e e n e x p o su re

a n d o u t-

c o m e , st u d ie s d id

n o t a c c o u n t fo r th e d u ra ti o n

o f p a rt ic ip a ti o n in

p ro g ra m

a n d b e n e fi ts

re c e iv e d

C o n si st e n t fi n d in g s

M o d e ra te ; B M I

u se d to

m e a su re

o b e si ty

L o w

to m o d e ra te

p re c is io n , n o a d ju st m e n t fo r

c o n fo u n d in g e ff e c t o f ti m e , e st im a te s w e re

lo w

to m o d e ra te

L o w -m

o d e ra te

L o n g it u d in a l (3 )

L o ss

o f p a rt ic ip a n ts

w a s n o t su b st a n ti a l, lo n g -

it u d in a l d e si g n a ll o w e d to

o b se r v e e ff e c t o f ti m e

o n w e ig h t g a in

C o n si st e n t fi n d in g s

M o d e ra te , B M I

u se d to

m e a su re

o b e si ty

L o w

to m o d e ra te

p re c is io n , a d ju st e d fo r ti m e ,

e st im a te s w e re

p re c is e w it h n a rr o w

c o n fi d e n c e

in te r v a ls

M o d e ra te

O b e si ty

in

c h il d re n (8 )

C ro ss -s e c ti o n al (5 )

S e lf -s e le c ti o n in

S N A P, n o e v id e n c e o f a te m -

p o ra l re la ti o n sh ip

b e tw

e e n e x p o su re

a n d o u t-

c o m e , st u d ie s d id

n o t a c c o u n t fo r th e d u ra ti o n

o f p a rt ic ip a ti o n in

p ro g ra m

a n d b e n e fi ts

re c e iv e d

C o n si st e n t fi n d in g s

M o d e ra te , B M I

u se d to

m e a su re

o b e si ty

L o w

to m o d e ra te

p re c is io n , n o a d ju st m e n t fo r

c o n fo u n d in g e ff e c t o f ti m e , e st im a te s w e re

p re c is e w it h n a rr o w

c o n fi d e n c e in te r v a ls a n d

st a n d a rd

e rr o rs .

L o w -m

o d e ra te

L o n g it u d in a l (6 )

S e lf -s e le c ti o n in

S N A P, lo ss

o f p a rt ic ip a n ts

w a s

n o t su b st a n ti a l, lo n g it u d in a l d e si g n a ll o w e d to

o b se r v e e ff e c t o f ti m e o n w e ig h t g a in

C o n si st e n t fi n d in g s

M o d e ra te , B M I

u se d to

m e a su re

o b e si ty

L o w

to m o d e ra te

p re c is io n , a d ju st e d fo r ti m e ,

ty p e o f a ss is ta n c e re c e iv e d , m a te rn a l a n d

h o u se h o ld

c h a ra c te ri st ic s, sm

a ll st a n d a rd

e rr o rs

M o d e ra te

A b b re vi a ti o n : B M I, b o d y m a ss

in d e x .

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other studies showed that participation was linked to

higher BMI in men.60,73 In a study of 435 low-income

individuals, receipt of SNAP benefits was related to a

higher BMI.60 Also, Baum et al, documented that long-

term participation in SNAP was associated with a 15%

increase in the risk of obesity. However, this effect was not

significant over a short and moderate period of 9 and 9–23

months, respectively.73

The effect of increased consumption of healthy foods

on weight gain has been documented. In a simulation

study by Han et al, a 20% reduction in the price of fruits

and vegetables produced a greater reduction in BMI in

SNAP participants vs nonparticipants.74 Similarly, a sto-

chastic microsimulation analysis study observed a

decrease in the incidence of obesity by 0.2% when fruits

and vegetables were subsidized in SNAP clients.75 Thus, a

reduction in prices of healthy foods might increase their

consumption and lead to a reduction in the risk of obesity.

Discussion SNAP is invaluable in terms of alleviating hunger and

food insecurity in low-income households. Yet, it is diffi-

cult to evaluate whether SNAP influences the types of

foods purchased as food choices are affected by numerous

factors in the low-income, which include the cost of food,

household size, nutrition knowledge, availability of fresh

foods in the neighborhood, transportation, and cultural

factors.8 Factors affecting food choices in low-income

groups were evaluated using the focus group study held

in six cities across the United States. The study included

28 focus groups of SNAP recipients from various ethnic

groups (white non-Hispanics, African Americans,

Hispanics), both working and non-working groups, as

well as individuals from rural and urban areas. This

focus group study included a diverse sample from all

different groups and regions. However, a major limitation

of focus group investigations is that participants may be

hesitant to express their personal and honest opinions,

from the fear of judgment or opposition from others.

SNAP has the potential to impact food choices due to

issues with the design of the program. The benefit amount

does not change in different geographic regions of the

country, despite the varying cost of foods. Thus, the pur-

chase of high cost, nutrient-rich foods such as fruits and

vegetables may be compromised in areas where the cost of

foods are higher.16 Although some adjustments exist in

whether the way the net income is calculated in areas

with a higher cost of living, evidence is limited as to

whether these adjustments are sufficient to compensate

for the greater expenditures on foods.24 Also, the benefits

that are set based on the TFP may be too low for partici-

pants to purchase a healthy diet.4 This is particularly true

because of the implicit assumption in the plan that SNAP

clients prepare a large proportion of their foods from raw

food materials.4

Another problem is that the distribution of benefits

once a month may result in a “feast and famine cycle”

among some SNAP participants.76 By the end of the

month, the nutritional quality of the diet may have

declined.26 Demonstration projects are needed to evaluate

the impact of a bi-monthly distribution pattern on food

expenditure and consumption in different demographic

settings among SNAP participants.

Previous studies have analyzed grocery receipts or

point of sale data for capturing food expenditure patterns

in SNAP participants. Most of the studies reported differ-

ences between types of foods purchased and total spending

in SNAP vs non-SNAP households. However, SNAP

households may be different from households that do not

participate in ways that affect their purchases. SNAP

households have been found to be more disadvantaged,

with a lower monthly income as compared to SNAP eli-

gible nonparticipants households.29 In addition, family

heads in the SNAP families were less educated and less

likely to be employed as compared to heads in non-SNAP

households.29 SNAP households were found to contain

more children than eligible nonparticipant households.29

It is possible that the availability of free lunches and

breakfasts at school, enables SNAP households with chil-

dren to spend relatively less on food.29 Finally, all the food

expenditure studies were cross-sectional in nature; as a

result, no causal association between SNAP participation

and food expenditures or choices can be inferred.

Whether diet quality differs between SNAP and income-

eligible non-SNAP participants remain unclear. While most

of these studies analyzed national datasets, a few have uti-

lized local convenience samples.51,58 In addition, differences

existed in populations studied ranging from children,52,77

adults,42,55,56 to the elderly.51,56 The variability in study

designs, methodology, and sample makes it difficult to com-

pare. The results from national samples document similar or

low dietary quality in adult SNAP participants as compared

to non-recipients; however, the diets of children on SNAP

were similar to low-income nonparticipants. This disparity

could be explained by the participation of low-income chil-

dren in additional programs such as the National School

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Lunch Program and the School Breakfast Program.78 It

should be noted that most of the dietary studies were cross-

sectional in design, limiting the ability to establish a causal

link between SNAP participation and poor diet quality or

nutrient intake.

Self-selection is another factor that limits the ability to

infer causality between SNAP participation and diet qual-

ity. Only 60% of the income-eligible recipients participate

in food assistance programs and many report no need for

nutrition assistance.79 Previous research has reported that

SNAP nonparticipants may have attained higher

education,80 live in higher-income neighborhoods,81 and

have other sources of financial support. Leung et al, indi-

cated that individuals who participate in SNAP may have

greater poverty and food insecurity and receive inadequate

nutrition than those who do not.82

The results of obesity studies are inconclusive. But

why is there evidence that SNAP is associated with obe-

sity? One suggestion has been that the absence of restric-

tions on the purchase of SSBs, or foods that are higher in

sugar and fat, may increase consumption of these foods.83

Also, the “food stamp cycle” of receiving stamps just once

a month in the beginning, and a decline in benefits over the

time of distribution, could lead to periods of overconsump-

tion and underconsumption and alternating patterns of

energy intakes have been linked to overweight.20

The studies that link SNAP participation and obesity

may serve as a proxy measure for food choices; however,

these are confounded by variables of demographics, food

insecurity, poverty, and self-selection. It has been docu-

mented that SNAP participation ensures an adequate quan-

tity of food but may or may not ensure the quality of food

purchased and consumed.43 Participants have shown

increased consumption of sources of added sugars and

solid fats.44,47,55 Nevertheless, the results should be inter-

preted with caution since these cross-sectional studies84,85

consider data at one point in time,60,61,84 but weight gain

results from long-term imbalances of energy.66 Relatively

few studies have investigated the effect of duration of the

participation and obesity.62,63,67,70,73 Also, many factors

may confound the association between exposure to

SNAP and the risk for obesity such as duration of expo-

sure and amount of benefits received. Further, differences

might exist in the characteristics of people who participate

for longer periods vs those that enroll for a shorter dura-

tion. Future studies could use statistical techniques such as

random-effects model to address the problem of bias with

self-selection into the program for varying durations.

Although the data on the increased consumption of

SSB’s in SNAP vs non-SNAP are inconclusive, the unrest-

ricted purchase of SSB’s with SNAP benefits may have

contributed to this increased trend of consumption.

Policymakers have proposed imposing sales taxes on

SSBs or restricting purchase of these beverages with

SNAP benefits.86 A study used point-of-sale data from

58 stores to estimate the changes in demand on taxation

of sugar-sweetened among SNAP users.87 The taxation of

SSBs reduced purchase and shifted focus toward non-

taxed beverages of bottled water and milk.87 The implica-

tions of restricting the purchase of SSB’s through SNAP

funds also are unclear. On the contrary, it is plausible that

some SNAP recipients might shift on to using their own

cash to purchase these items,88 or food manufacturers may

create similar drinks with the same added sugar composi-

tion, which would not fall under the restricted category.89

In addition, it is unknown whether there is a significant

difference in soda consumption between SNAP partici-

pants and nonparticipants that extends beyond the risk

linked to poverty.90 More demonstration projects or pilot

studies are warranted in order to obtain evidence on how

such restrictions would affect choices for SSB.

Another point of consideration is that SNAP provides

benefits for food purchases and not actual cash. The

Southworth hypothesis provides distinctions between two

groups of food stamp coupon recipients: inframarginal reci-

pients, or those that have food expenditures higher than the

value of the coupon benefits, and extramarginal recipients,

whose food expenditures are less than or equal to the value

of their coupon benefits.91 The hypothesis maintains that

inframarginal recipients should choose the same amount

and type of goods whether they receive food stamps or

cash.91 Research on the Southworth hypothesis is inconclu-

sive, with some investigations in support92,93 and other

against this system.94,95 In an investigation by Lusk et al,

in-kind transfers had a similar effect on food purchasing

patterns as an unrestricted cash transfer for inframarginal

consumers, but expenditures on food were higher for in-

kind than cash transfers for extramarginal consumers.96 In

the same study, which had a restriction on the purchase of

SSBs with SNAP benefits, those who always purchased

soda did so even after the restriction. They reported rearran-

ging items purchased in cash or kind, with no extra cost.

Thus, an increase in the food income through SNAP bene-

fits or restriction of purchase of certain food items such as

SSBs with SNAP benefits, might lead to varying purchasing

patterns in different types of consumers.

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Behavioral economic strategies to encourage SNAP

recipients to select healthy choices could reduce cost of

healthy foods,97 providing monetary benefits for the pur-

chase of healthy foods,97 increasing the SNAP benefits,

issuing SNAP benefits twice a month,98 incentivizing

small food retailers to offer more food choices in low-

income neighborhoods,98 increasing grocery stores and

supermarkets in poor neighborhood and strengthening the

SNAP-Ed program. Establishing supermarkets in low-

income areas can be both costly and time-consuming.99

Also, a lack of transportation can still constitute a major

barrier to accessing the supermarkets, thereby increasing

the dependence on corner stores in low-income areas.100

Significantly higher purchase of unhealthy foods has been

associated with shopping at corner stores as compared to

that at the supermarkets.101 Results from the Baltimore

Healthy Stores pilot project indicate that interventions

targeted toward strengthening small food-based stores are

effective in increasing healthy food availability in low-

income neighborhoods.100

Which of the above strategies would be most effective

is unclear, but economic incentives are certainly desirable.

The USDA estimated that a decrease in the price of fruits

and vegetables by 10%, would increase purchases of fruits

and vegetables by 6–7% in SNAP participants.38

Blumenthal et al, surveyed 1250 individuals who had

stakeholder interest in SNAP from different fields such

as academia, advocacy groups, government, and the food

industry.102 Respondents proposed the use of incentives in

the form of vouchers and coupons to purchase healthy

foods. An example of providing an economic benefit is

seen in the Healthy Incentives Pilot randomized trial. The

SNAP participants received 30 cents on every one dollar

they spent on the targeted lists of fruits and vegetables.103

This resulted in a 26% increase in consumption of these

foods.103 Another such initiative was the Double Up Food

Bucks Program, in which SNAP participants received a

$10 gift card, when they spent $10 on the purchase of

fruits and vegetables in a single transaction. This program

resulted in a rise in expenditure on fruits and vegetables by

$0.40 per month. However, the effect deteriorated on with-

drawal of the financial incentives, thereby indicating that

the program did not produce long-lasting effects on the

food choices and purchasing behaviors of the

participants.104

Another suggestion is that an escalation in the total

SNAP benefits would increase the spending on food in

general. Greater spending on food is associated with

higher consumption of fruits and vegetables for both

SNAP participants and eligible nonparticipants.40 Lin et

al, observed a rise in the consumption of fruits and vege-

tables with additional SNAP income, but the total intake

was still below recommended levels. It is unclear whether

an increase in SNAP benefits would result in greater con-

sumption of these foods to match the level of the dietary

recommendations.

The Farm Bill of 2014 requires SNAP-authorized retail

stores to offer a larger variety of healthy food options.98

This provision will increase the access and availability of

healthy foods to the beneficiaries. Yet, there might be

problems associated with this approach as SNAP recipi-

ents may not be inclined to purchase these items, which

may levy an economic strain on the retailer.98 Finally,

increasing the availability of supermarkets and grocery

stores in low-income neighborhoods may also be effective

in reducing dependence on convenience stores or gas sta-

tions that do not offer adequate healthy food choices.98

There are some actions that can be taken by the gov-

ernment to improve the adequacy of the SNAP. One such

action is to replace the TFP with the Low-Cost Food

Plan.105 TFP is based on a national average of food prices;

however, there is a wide variation in the food prices across

the nation. As a result, higher food prices may limit the

affordability of a healthy diet with the same SNAP benefits

as compared to participants living in areas where food

prices are low.105 Low-Cost Program is approximately

30% higher than the TFP, depending on household

composition.105 Although the Low-Cost Food Plan, shares

many limitations of the TFP, it is more in aligned to what

low- and moderate-income families spend on food.105 In

addition, it provides greater food choices for healthy

foods.106

The current practice of the distribution of SNAP ben-

efits is that they are received once a month and most of the

funds are used in the first two weeks of receiving them.

This pattern promotes the purchase of packaged goods that

have a longer shelf life and can be used on a later date. A

bi-monthly distribution of benefits may increase the pur-

chase of perishable food items, and reduce the decline in

diet quality by the end of the month.76

Encouraging alternate retailers such as mobile markets/

vans and community gardens, which can increase access to

healthy food options for groups who lack or have limited

transportation for grocery shopping at supermarkets.102

Another promising strategy is strengthening SNAP-Ed,

the nutrition education programs funded through SNAP.

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Two demonstration projects were undertaken by the USDA

to increase the consumption of fruits and vegetables in

SNAP recipients. The Building and Strengthening Iowa

Community Support program delivered nutrition and phy-

sical activity education to elementary school children,107

while the Michigan State University Extension’s Eat

Smart, Live Strong program provided education to low-

income seniors.108 Both these programs increased the con-

sumption of SNAP recipients for fruits and vegetables by

0.24 cups in children and 0.52 cups in the seniors,

respectively.108 Thus, expansion of SNAP-Ed could play a

valuable role in improving nutrition knowledge on under-

standing food labels, choosing healthy sources of macronu-

trients and micronutrients in the diet, identification of

sources of added sugars, and ways to prepare balanced

meals.109

Key research gaps There is a lack of investigations that directly investigate

food choices in SNAP participants. This research reviewed

and summarized evidence on food choices as assessed

from proxy measures of food expenditures, diet quality,

and obesity in SNAP participants. However, this might not

be the best approach as food choices are influenced by a

number of individual, demographic, and socio-economic

factors.

There was a wide diversity in the studies reviewed in

terms of the design and sample sizes. They ranged from

focus groups, convenience samples, national samples,

cross-sectional studies to economic projection investiga-

tions. While focus group studies are qualitative in nat-

ure, cross-sectional studies could provide limited

quantitative evidence on the causal relationship between

SNAP participation and diet quality or obesity.

However, this review has included a grading system to

evaluate the grading of the evidence regarding different

outcomes to provide more transparency and structure to

the review.

There was a lack of consensus on the definitions and

standards for measurements of outcomes such as diet

quality. Diet quality is measured using different question-

naires and tools, making the standardization and interpre-

tation of results difficult. These inconsistencies made it

difficult to combine results across studies.

In summary, future research should use case-controlled

or prospective study designs in low-income individuals to

investigate the effects of participation in SNAP on food

choices.

Conclusions The SNAP in the US serves as an important safety net

program for millions of low-income Americans. As such it

has the potential to impact food choices and dietary

intakes of a large segment of society. However, food

choices are guided by factors other than socio-economic

and demographic characteristics. These include human

behavior with the biological propensity to eat foods high

in sugar, salt, and fat. In addition, there are significant

missed opportunities that limit the full potential of SNAP

to promote the consumption of nutritious foods by SNAP

recipients. Certain immediate steps can be taken by the

government. These include replacing the TFP with the

Low-Cost Food Plan; changing the pattern of distribution

of SNAP benefits to bi-monthly; incentivizing food retai-

lers to offer more food choices in low-income neighbor-

hoods; encouraging mobile vendors to provide healthy

foods to individuals who lack transportation; and strength-

ening the SNAP-Ed program. Finally, it would be bene-

ficial to support pilot and demonstration projects to obtain

evidence on policies of limiting SNAP benefits for the

purchase of unhealthy foods.

Acknowledgments This research was funded by the Bess Heflin Centennial

Professorship #3086006875. The funder provided funds

for conducting the study.

Disclosure The authors report no conflicts of interest in this work.

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