Literature DB >> 28424188

Nutritional profile of Supplemental Nutrition Assistance Program household food and beverage purchases.

Anna H Grummon1,2, Lindsey Smith Taillie3,2.   

Abstract

Background: The Supplemental Nutrition Assistance Program (SNAP), which is the largest federal nutrition assistance program in the United States, serves nearly 1 of 7 Americans. To date, few studies have examined food and beverage purchase behaviors in SNAP participants with the use of electronic purchase data.Objective: In this cross-sectional study, we examined household store purchases of key food, beverage, and nutrient groups in SNAP participants and nonparticipants.Design: Using a data set of US households' (n = 98,256 household-by-quarter observations) packaged food and beverage purchases and SNAP status [current participant, income-eligible nonparticipant (income ≤130% of the Federal Poverty Level [FPL]), and higher-income nonparticipants (income >130% of the FPL)] from 3 quarters during 2012-2013, we estimated pooled ordinary least-squares models, clustered at the household level, to examine the association between SNAP status and purchases while controlling for sociodemographic characteristics. We examined purchases of health- and policy-relevant food and beverage groups [e.g., fruit and sugar-sweetened beverages (SSBs)] and nutrients (e.g., total calories and sodium).
Results: Regardless of SNAP status, households had low mean purchases of fruit, vegetables, and fiber and high mean purchases of junk foods, saturated fat, and sodium. After adjustment for multiple comparisons and demographic characteristics, we found significant differences by SNAP status of purchases of fruit, processed meat, salty snacks, sweeteners and toppings, SSBs, and total calories, fiber, sugar, and sodium. Several of these differences were clinically important. For example, compared with income-eligible and higher-income nonparticipants, SNAP participants purchased an additional ∼15-20 kcal · person-1 · d-1 from SSBs (P < 0.0001) and ∼174-195 mg total Na · person-1 · d-1 (P <0.0001). Results were robust to corrections for sample-selection bias and to the exclusion of observations with potentially misreported SNAP status.Conclusions: American households, including SNAP households, show room for improvement in the nutritional quality of store purchases. New interventions and policies may be needed to improve food and beverage purchases in both SNAP and non-SNAP households.
© 2017 American Society for Nutrition.

Entities:  

Keywords:  Supplemental Nutrition Assistance Program; big data; diet quality; food and beverage purchases; food-purchase data; health disparities; income disparities; low income; nutrients

Mesh:

Year:  2017        PMID: 28424188      PMCID: PMC5445673          DOI: 10.3945/ajcn.116.147173

Source DB:  PubMed          Journal:  Am J Clin Nutr        ISSN: 0002-9165            Impact factor:   7.045


  35 in total

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Authors:  Lenny R Vartanian; Marlene B Schwartz; Kelly D Brownell
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2.  American adults eligible for the Supplemental Nutritional Assistance Program consume more sugary beverages than ineligible adults.

Authors:  Sara N Bleich; Seanna Vine; Julia A Wolfson
Journal:  Prev Med       Date:  2013-10-12       Impact factor: 4.018

3.  The importance of population-wide sodium reduction as a means to prevent cardiovascular disease and stroke: a call to action from the American Heart Association.

Authors:  Lawrence J Appel; Edward D Frohlich; John E Hall; Thomas A Pearson; Ralph L Sacco; Douglas R Seals; Frank M Sacks; Sidney C Smith; Dorothea K Vafiadis; Linda V Van Horn
Journal:  Circulation       Date:  2011-01-13       Impact factor: 29.690

4.  Impact and ethics of excluding sweetened beverages from the SNAP program.

Authors:  Anne Barnhill
Journal:  Am J Public Health       Date:  2011-05-12       Impact factor: 9.308

5.  Associations of food stamp participation with dietary quality and obesity in children.

Authors:  Cindy W Leung; Susan J Blumenthal; Elena E Hoffnagle; Helen H Jensen; Susan B Foerster; Marion Nestle; Lilian W Y Cheung; Dariush Mozaffarian; Walter C Willett
Journal:  Pediatrics       Date:  2013-02-25       Impact factor: 7.124

6.  Moderation of the Relation of County-Level Cost of Living to Nutrition by the Supplemental Nutrition Assistance Program.

Authors:  Sanjay Basu; Christopher Wimer; Hilary Seligman
Journal:  Am J Public Health       Date:  2016-09-15       Impact factor: 9.308

7.  Estimating the potential of taxes on sugar-sweetened beverages to reduce consumption and generate revenue.

Authors:  Tatiana Andreyeva; Frank J Chaloupka; Kelly D Brownell
Journal:  Prev Med       Date:  2011-04-03       Impact factor: 4.018

8.  Grocery store beverage choices by participants in federal food assistance and nutrition programs.

Authors:  Tatiana Andreyeva; Joerg Luedicke; Kathryn E Henderson; Amanda S Tripp
Journal:  Am J Prev Med       Date:  2012-10       Impact factor: 5.043

9.  Effects of Subsidies and Prohibitions on Nutrition in a Food Benefit Program: A Randomized Clinical Trial.

Authors:  Lisa Harnack; J Michael Oakes; Brian Elbel; Timothy Beatty; Sarah Rydell; Simone French
Journal:  JAMA Intern Med       Date:  2016-11-01       Impact factor: 21.873

Review 10.  Resolved: there is sufficient scientific evidence that decreasing sugar-sweetened beverage consumption will reduce the prevalence of obesity and obesity-related diseases.

Authors:  F B Hu
Journal:  Obes Rev       Date:  2013-06-13       Impact factor: 9.213

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  17 in total

1.  Comparing demographic and health characteristics of new and existing SNAP recipients: application of a machine learning algorithm.

Authors:  Rita Hamad; Zachary S Templeton; Lena Schoemaker; Michelle Zhao; Jay Bhattacharya
Journal:  Am J Clin Nutr       Date:  2019-04-01       Impact factor: 7.045

2.  Lowering the impact of food insecurity in African American adults with type 2 diabetes mellitus (LIFT-DM) - Study protocol for a randomized controlled trial.

Authors:  Rebekah J Walker; Rebecca G Knapp; Clara E Dismuke-Greer; Renee E Walker; Mukoso N Ozieh; Leonard E Egede
Journal:  Contemp Clin Trials       Date:  2020-11-07       Impact factor: 2.226

3.  Nutritional Profile of Purchases by Store Type: Disparities by Income and Food Program Participation.

Authors:  Lindsey Smith Taillie; Anna H Grummon; Donna R Miles
Journal:  Am J Prev Med       Date:  2018-06-15       Impact factor: 5.043

4.  Food, Big Data, and Decision-making: a Scoping Review-the 3-D Commission.

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5.  Differences in Dietary Quality by Sexual Orientation and Sex in the United States: NHANES 2011-2016.

Authors:  Carmen E Prestemon; Anna H Grummon; Pasquale E Rummo; Lindsey Smith Taillie
Journal:  J Acad Nutr Diet       Date:  2021-12-08       Impact factor: 5.234

6.  Supplemental Nutrition Assistance Program participation and racial/ethnic disparities in food and beverage purchases.

Authors:  Anna H Grummon; Lindsey Smith Taillie
Journal:  Public Health Nutr       Date:  2018-10-11       Impact factor: 4.022

7.  Socio-economic and racial/ethnic disparities in the nutritional quality of packaged food purchases in the USA, 2008-2018.

Authors:  Allison M Lacko; Joanna Maselko; Barry Popkin; Shu Wen Ng
Journal:  Public Health Nutr       Date:  2021-01-27       Impact factor: 4.022

8.  Trends in Food Insecurity in the United States from 2011-2017: Disparities by Age, Sex, Race/Ethnicity, and Income.

Authors:  Rebekah J Walker; Emma Garacci; Aprill Z Dawson; Joni S Williams; Mukoso Ozieh; Leonard E Egede
Journal:  Popul Health Manag       Date:  2020-09-17       Impact factor: 2.290

9.  Is less more? Examining the relationship between food assistance benefit levels and childhood weight.

Authors:  Megan M Reynolds; Ashley M Fox; Ming Wen; Michael W Varner
Journal:  SSM Popul Health       Date:  2020-03-25

10.  Food Insecurity is Associated with Maladaptive Eating Behaviors and Objectively Measured Overeating.

Authors:  Emma J Stinson; Susanne B Votruba; Colleen Venti; Marisol Perez; Jonathan Krakoff; Marci E Gluck
Journal:  Obesity (Silver Spring)       Date:  2018-11-14       Impact factor: 5.002

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