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Public health policies to encourage healthy eating habits: recent perspectives

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There is an urgent need to address unhealthy dietary patterns at the population level. Poor diet and physical inactivity are key drivers of the obesity pandemic, and they are among the leading causes of preventable death and disability in nearly every ...

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Trends in US home food preparation and consumption: analysis of national nutrition surveys and time use studies from 1965–1966 to 2007–2008

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It has been well-documented that Americans have shifted towards eating out more and cooking at home less. However, little is known about whether these trends have continued into the 21[st] century, and whether these trends are consistent amongst low-income ...

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Summary Approximately two thirds of U.S. adults and one fifth of U.S. children are obese or overweight. During 1980-2004, obesity prevalence among U.S. adults doubled, and recent data indicate an estimated 33% of U.S. adults are overweight (body mass index [BMI] 25.0-29.9), 34% are obese (BMI ≥30.0), including nearly 6% who are extremely obese (BMI ≥40.0). The prevalence of being overweight among children and adolescents increased substantially during 1999-2004, and approximately 17% of U.S. children and adolescents are overweight (defined as at or above the 95% percentile of the sex-specific BMI for age growth charts). Being either obese or overweight increases the risk for many chronic diseases (e.g., heart disease, type 2 diabetes, certain cancers, and stroke). Reversing the U.S. obesity epidemic requires a comprehensive and coordinated approach that uses policy and environmental change to transform communities into places that support and promote healthy lifestyle choices for all U.S. residents. Environmental factors (including lack of access to full-service grocery stores, increasing costs of healthy foods and the lower cost of unhealthy foods, and lack of access to safe places to play and exercise) all contribute to the increase in obesity rates by inhibiting or preventing healthy eating and active living behaviors. Recommended strategies and appropriate measurements are needed to assess the effectiveness of community initiatives to create environments that promote good nutrition and physical activity. To help communities in this effort, CDC initiated the Common Community Measures for Obesity Prevention Project (the Measures Project). The objective of the Measures Project was to identify and recommend a set of strategies and associated measurements that communities and local governments can use to plan and monitor environmental and policy-level changes for obesity prevention. This report describes the expert panel process that was used to identify 24 recommended strategies for obesity prevention and a suggested measurement for each strategy that communities can use to assess performance and track progress over time. The 24 strategies are divided into six categories: 1) strategies to promote the availability of affordable healthy food and beverages), 2) strategies to support healthy food and beverage choices, 3) a strategy to encourage breastfeeding, 4) strategies to encourage physical activity or limit sedentary activity among children and youth, 5) strategies to create safe communities that support physical activity, and 6) a strategy to encourage communities to organize for change.

Journal Information

The MMWR Recommendations and Reports contain in-depth articles that relay policy statements for prevention and treatment on all areas in CDC's scope of responsibility (e.g., recommendations from the Advisory Committee on Immunization Practices).

Publisher Information

Collaborating to create the expertise, information, and tools that people and communities need to protect their health - through health promotion, prevention of disease, injury and disability, and preparedness for new health threats. CDC seeks to accomplish its mission by working with partners throughout the nation and the world to * monitor health, * detect and investigate health problems, * conduct research to enhance prevention, * develop and advocate sound public health policies, * implement prevention strategies, * promote healthy behaviors, * foster safe and healthful environments, * provide leadership and training. Those functions are the backbone of CDC′s mission. Each of CDC′s component organizations undertakes these activities in conducting its specific programs. The steps needed to accomplish this mission are also based on scientific excellence, requiring well-trained public health practitioners and leaders dedicated to high standards of quality and ethical practice.

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Recommended Community Strategies and Measurements to Prevent Obesity in the United States on JSTOR

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Summary Approximately two thirds of U.S. adults and one fifth of U.S. children are obese or overweight. During 1980-2004, obesity prevalence among U.S. adults doubled, and recent data indicate an estimated 33% of U.S. adults are overweight (body mass index [BMI] 25.0-29.9), 34% are obese (BMI ≥30.0), including nearly 6% who are extremely obese (BMI ≥40.0).

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Dariush Mozaffarian,

Junxiu Liu,

Stephen Sy,

Yue Huang,

Colin Rehm,

Yujin Lee,

Parke Wilde,

Shafika Abrahams-Gessel,

Thiago de Souza Veiga Jardim,

Tom Gaziano

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Abstract

Background

The Supplemental Nutrition Assistance Program (SNAP) provides approximately US$70 billion annually to support food purchases by low-income households, supporting approximately 1 in 7 Americans. In the 2018 Farm Bill, potential SNAP revisions to improve diets and health could include financial incentives, disincentives, or restrictions for certain foods. However, the overall and comparative impacts on health outcomes and costs are not established. We aimed to estimate the health impact, program and healthcare costs, and cost-effectiveness of food incentives, disincentives, or restrictions in SNAP.

Methods and findings

We used a validated microsimulation model (CVD-PREDICT), populated with national data on adult SNAP participants from the National Health and Nutrition Examination Survey (NHANES) 2009-2014, policy effects from SNAP pilots and food pricing meta-analyses, diet-disease effects from meta-analyses, and policy, food, and healthcare costs from published literature to estimate the overall and comparative impacts of 3 dietary policy interventions: (1) a 30% incentive for fruits and vegetables (F&V), (2) a 30% F&V incentive with a restriction of sugar-sweetened beverages (SSBs), and (3) a broader incentive/disincentive program for multiple foods that also preserves choice (SNAP-plus), combining 30% incentives for F&V, nuts, whole grains, fish, and plant-based oils and 30% disincentives for SSBs, junk food, and processed meats. Among approximately 14.5 million adults on SNAP at baseline with mean age 52 years, our simulation estimates that the F&V incentive over 5 years would prevent 38,782 cardiovascular disease (CVD) events, gain 18,928 quality-adjusted life years (QALYs), and save $1.21 billion in healthcare costs. Adding SSB restriction increased gains to 93,933 CVD events prevented, 45,864 QALYs gained, and $4.33 billion saved. For SNAP-plus, corresponding gains were 116,875 CVD events prevented, 56,056 QALYs gained, and $5.28 billion saved. Over a lifetime, the F&V incentive would prevent approximately 303,900 CVD events, gain 649,000 QALYs, and save $6.77 billion in healthcare costs. Adding SSB restriction increased gains to approximately 797,900 CVD events prevented, 2.11 million QALYs gained, and $39.16 billion in healthcare costs saved. For SNAP-plus, corresponding gains were approximately 940,000 CVD events prevented, 2.47 million QALYs gained, and $41.93 billion saved. From a societal perspective (including programmatic costs but excluding food subsidy costs as an intra-societal transfer), all 3 scenarios were cost-saving. From a government affordability perspective (i.e., incorporating food subsidy costs, including for children and young adults for whom no health gains were modeled), the F&V incentive was of low cost-effectiveness at 5 years (incremental cost-effectiveness ratio: $548,053/QALY) but achieved cost-effectiveness ($66,525/QALY) over a lifetime. Adding SSB restriction, the intervention was cost-effective at 10 years ($68,857/QALY) and very cost-effective at 20 years ($26,435/QALY) and over a lifetime ($5,216/QALY). The combined incentive/disincentive program produced the largest health gains and reduced both healthcare and food costs, with net cost-savings of $10.16 billion at 5 years and $63.33 billion over a lifetime. Results were consistent in probabilistic sensitivity analyses: for example, from a societal perspective, 1,000 of 1,000 iterations (100%) were cost-saving for all 3 interventions. Due to the nature of simulation studies, the findings cannot prove the health and cost impacts of national SNAP interventions.

Conclusions

Leveraging healthier eating through SNAP could generate substantial health benefits and be cost-effective or cost-saving. A combined food incentive/disincentive program appears most effective and may be most attractive to policy-makers.

Author summary

Why was this study done?

The US Supplemental Nutrition Assistance Program (SNAP) provides approximately $70 billion/year for food purchases for low-income households, supporting about 1 in 7 Americans.

Diet-related diseases and costs disproportionately harm low-income individuals, making SNAP a major potential lever to improve food choices, reduce healthcare costs, and improve disparities.

However, the overall and comparative impacts and cost-effectiveness of food incentives, disincentives, and restrictions in SNAP for health outcomes and costs are not established.

What did the researchers do and find?

Using nationally representative data and a validated microsimulation model, we evaluated 3 potential interventions within SNAP, including (1) a 30% subsidy for purchases of fruits and vegetables (F&V) (F&V incentive), (2) a F&V incentive with restriction of sugar-sweetened beverages (SSBs) (F&V incentive/SSB restriction), and (3) a broader incentive/disincentive program that preserves choice (SNAP-plus), combining a 30% subsidy for purchases of F&V, nuts, whole grains, fish, and plant-based oils and a 30% disincentive for purchases of SSBs, junk food, and processed meats.

Over a lifetime, we found that the F&V incentive, F&V incentive/SSB restriction, and SNAP-plus programs would prevent respectively about 304,000, 798,000, and 940,000 CVD events and −1,000, 171,000, and 147,000 cases of diabetes, with respective healthcare cost-savings of about $7 billion, $39 billion, and $429 billion. From a societal perspective, all interventions were cost-saving. From a government affordability perspective (incorporating food subsidy costs for all 42 million participants in SNAP, including children), the F&V incentive was cost-effective (lifetime incremental cost-effectiveness ratio [ICER] approximately $66,500 per quality-adjusted life year), the F&V incentive/SSB restriction policy was very cost-effective (ICER approximately $5,200), and the SNAP-plus program was cost-saving (saving $63 billion).

What do these findings mean?

Leveraging healthier eating through SNAP could generate substantial health benefits among low-income Americans and be cost-effective or cost-saving.

A combined food incentive/disincentive program produced the largest modeled gains in health and healthcare savings.

These findings support the need to test these SNAP interventions in state pilot programs.

Citation: Mozaffarian D, Liu J, Sy S, Huang Y, Rehm C, Lee Y, et al. (2018) Cost-effectiveness of financial incentives and disincentives for improving food purchases and health through the US Supplemental Nutrition Assistance Program (SNAP): A microsimulation study. PLoS Med 15(10): e1002661. https://doi.org/10.1371/journal.pmed.1002661

Academic Editor: Sanjay Basu, Stanford University, UNITED STATES

Received: March 6, 2018; Accepted: August 30, 2018; Published: October 2, 2018

Copyright: © 2018 Mozaffarian et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Data from the National Health and Nutrition Examination Survey (NHANES) are publicly available at https://www.cdc.gov/nchs/nhanes/; other data inputs are from published papers or published in the present manuscript.

Funding: This research was supported by the NIH, NHLBI (R01 HL130735, PI RM; R01 HL115189, PI DM). In addition, JL was supported by an American Heart Association postdoctoral fellowship (17POST33670808). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: I have read the journal's policy and the authors of this manuscript have the following competing interests: RM reports research funding from Unilever and personal fees from the World Bank and Bunge; and DM personal fees from Amarin, Acasti Pharma, GOED, DSM, Nutrition Impact, Pollock Communications, Bunge, Indigo Agriculture, America's Test Kitchen, and UpToDate; all outside the submitted work.

Abbreviations: BMI, body mass index; CHD, coronary heart disease; CVD, cardiovascular disease; EBT, electronic benefits transfer; F&V, fruits and vegetables; HDL-cholesterol, high-density lipoprotein cholesterol; HIP, Healthy Incentives Pilot; ICER, incremental cost-effectiveness ratio; LDL-cholesterol, low-density lipoprotein cholesterol; NHANES, National Health and Nutrition Examination Survey; QALY, quality-adjusted life year; SNAP, Supplemental Nutrition Assistance Program; SSB, sugar-sweetened beverage; UCP, Universal Product Code; UI, uncertainty interval; USDA, US Department of Agriculture

Introduction

Suboptimal diet is a major cause of poor health, particularly related to cardiometabolic diseases such as coronary heart disease (CHD), stroke, type 2 diabetes, and obesity [ 1, 2]. Nearly half of all cardiovascular and diabetes deaths in the US are linked to poor diet, corresponding to almost 1,000 deaths daily [ 3]. These adverse health conditions produce tremendous economic burdens [ 2, 4], including for government programs such as Medicare and Medicaid, private health insurers, and self-insured businesses, and for individuals in the form of personal illness, out-of-pocket costs, and lost productivity. The direct and indirect costs of cardiovascular diseases (CVDs) are estimated at $317 billion/year, including $193 billion/year in direct healthcare expenditures and $124 billion/year in lost productivity, while the partly overlapping corresponding costs of type 2 diabetes are $320, $112, and $208 billion/year, respectively. The combined healthcare costs of all obesity-related conditions are estimated at $1.42 trillion/year, or about 8% of US gross domestic product. Further, these diet-related diseases and costs disproportionately harm low-income individuals [ 4], contributing to major disparities. Clearly, new cost-effective interventions are needed to address these burdens.

The US Supplemental Nutrition Assistance Program (SNAP, formerly the Food Stamp Program) is among the largest and most promising opportunities to improve food choices and health while reducing costs and disparities. Established by the Food Stamp Act in 1964 "to safeguard the health and well-being of the Nation's population and raise levels of nutrition among low-income households" [ 5], SNAP provides monthly benefits for retail food purchases to approximately 42 million low-income individuals, or approximately 1 in 7 Americans [ 6, 7]. While SNAP is an essential safety net that successfully reduces food insecurity [ 8], relatively little programmatic emphasis has been on nutrition or health. Disparities remain or have worsened in the diets of low-income Americans [ 9- 12], and SNAP participants experience significantly higher all-cause, cardiovascular, and diabetes mortality compared to other American adults [ 13- 15]. The size, scope, and potential for impact of SNAP are staggering, with an annual budget of $68 billion/year that exceeds those of the National Institutes of Health ($33 billion), Centers for Disease Control and Prevention ($7 billion), and Food and Drug Administration ($5 billion) combined [ 7].

SNAP is reauthorized by Congress every 5 years as part of the Farm Bill, due this year. With its size and reach, the 2018 Farm Bill represents one of the most timely and compelling opportunities to improve the nation's health. Tremendous current interest exists in potential ways to further leverage SNAP to not only address food insecurity but also improve nutrition, health, equity, and health costs [ 16- 23]. For example, a new bipartisan Food Is Medicine Working Group was recently launched in the US Congress to create innovative policies, including within SNAP, to leverage food policy to improve health and to lower healthcare costs [ 16]. Separately, the influential Bipartisan Policy Center recently created a new SNAP Task Force, co-chaired by former Senator Bill Frist and former US Department of Agriculture (USDA) Secretaries Dan Glickman and Ann Veneman, to provide policy recommendations for using SNAP and related programs to improve health, combat poor nutrition, and reduce healthcare costs [ 17]. Previously proposed revisions for SNAP have included incorporating financial incentives for purchasing fruits, vegetables, or other healthful foods, and restricting unhealthy items such as sugar-sweetened beverages (SSBs) [ 18- 23]. The USDA Healthy Incentives Pilot (HIP), a randomized controlled trial implemented among SNAP participants, demonstrated that a 30% subsidy for fruits and vegetables (F&V) increased intake by approximately 26% [ 24]. Incentives for healthful foods could also be combined with partial disincentives on less healthful items, rather than absolute restrictions, preserving choice for participants. Yet, whereas economic incentives and disincentives are known to be effective in changing behavior [ 24- 27], the overall and comparative impacts of these different strategies on health outcomes, program and healthcare costs, and cost-effectiveness are not established, limiting policy decision-making.

Policy-makers, practitioners, and the public are concerned about potential federal changes to programs that aid Americans, requiring new research and data to support how SNAP benefits may be strengthened and leveraged in a way that benefits participants and appeals to lawmakers. Understanding not only health impacts, but also costs and cost-effectiveness, is a crucial tool for such conversations. To elucidate the effects of different strategies to improve SNAP and provide evidence to help inform SNAP policy, a validated microsimulation model was used to estimate the potential health impact of a F&V incentive, a F&V incentive plus SSB restriction, and a combined incentive/disincentive program on multiple foods within SNAP on cardiometabolic outcomes. Furthermore, the corresponding programmatic and healthcare costs and comparative cost-effectiveness of these different strategies were evaluated. This work was performed as part of the Food Policy Review and Intervention Cost-Effectiveness (Food-PRICE) project ( https://www.food-price.org/).

Methods

Study overview

The potential health and economic impacts of implementing specific food incentive, restriction, or disincentive programs within SNAP were modeled using the validated CVD-PREDICT microsimulation model [ 28, 29] over a 5-, 10-, and 20-year analytic period (2018-2038) as well as over the cohort lifetime. Populated with individuals from the National Health and Nutrition Examination Survey (NHANES), the CVD-PREDICT model utilized national data on baseline characteristics, risks, dietary habits, and disease incidence to assess cumulative cardiometabolic health outcomes and costs based on current trends as well as alternative scenarios of specific interventions ( Fig 1; S1 Text). At each stage of the logic pathway, the best available evidence, supplemented with reasoned assumptions, was used to estimate the potential health and economic effects of the respective food price policies implemented among low-income Americans participating in SNAP. The elements of the model inputs, structure, and outputs are each described in further detail below. This investigation utilized de-identified datasets and was exempt from human subjects approval.

SNAP intervention scenarios

We modeled 3 distinct interventions within SNAP, including (1) a 30% financial incentive for purchases of F&V (F&V incentive), (2) a 30% F&V incentive with restriction of SSBs (F&V incentive/SSB restriction), and (3) a broader incentive/disincentive program that preserves choice (SNAP-plus), combining a 30% financial incentive for purchases of F&V, nuts, whole grains, fish, and plant-based oils and a 30% disincentive for purchases of SSBs, junk food, and processed meats (see S1 Table for food category details and definitions). These 3 interventions were predefined based on current policy evidence and interest in the US [ 17- 24, 27, 30] (F&V incentives and SSB restrictions) and worksite wellness and insurance benefit programs that incentivize a broad range of healthy foods (SNAP-plus) [ 31, 32], focusing on foods with the largest estimated cardiometabolic impact in the US [ 3]. We performed a secondary analysis of SNAP-plus excluding incentives for fish and plant-based oils, the 2 most expensive categories.

We modeled the implementation of each intervention through use of the existing SNAP electronic benefits transfer (EBT) card, which can be used like a debit card at diverse retail outlets including supermarkets, grocery stores, convenience stores, and farmers markets and is linked to product-identifying Universal Product Codes (UPCs). Use of EBT takes advantage of this established system with existing product-specific inclusions and restrictions (e.g., purchases of alcohol and preprepared hot foods are excluded), permitting technology-based financial nudges. In SNAP-plus, for example, for every SNAP dollar spent on incentivized foods, $0.30 would be returned to the EBT account (producing a net additional expense to SNAP), while for every SNAP dollar spent on disincentivized foods, $1.30 would be debited from the EBT card (producing a net relative savings to SNAP that could help offset the cost of incentives). Each intervention was accompanied by modeled coordinated outreach and education to SNAP participants, accounted for in policy effect sizes and implementation and ongoing annual costs (see below).

Simulated US population

Our population was based on Americans aged 35-80 years at baseline across 3 cycles of NHANES (2009-2010, 2011-2012, 2013-2014) who participated in SNAP, defined by reporting household SNAP benefits in the past 12 months ( Table 1). The sociodemographic characteristics, cardiometabolic risk factors, and dietary habits of these participants were derived from NHANES data, including dietary habits from two 24-hour recalls per person as previously described [ 9]. Utilizing NHANES survey weights to account for the complex survey design and sampling, we sampled the study population with replacement to create a simulated nationally representative SNAP population of 1,000,000 individuals, who were followed until death or age 100, whichever came first. We recognized that individuals may enter and leave SNAP over time, and considered this impact as further described in S2 Text. Data from NHANES are publicly available at https://www.cdc.gov/nchs/nhanes/; other data inputs are from published papers or published in the present report.

Policy effects on dietary habits

Details of the sources for policy effects and corresponding calculations are provided in S2 Table. Estimated policy effects accounted for the expected change in intake of each food item for a 30% price change, the percent of overall food purchased using SNAP dollars, the percent of each food item purchased at SNAP venues (e.g., supermarkets, grocery stores, and farmers markets, as opposed to restaurants and food banks), and the expected shifting in food spending from SNAP to other food dollars as a result of the intervention. Consistent with empirical results of interventions that demonstrate rapid changes in consumption with price changes [ 24- 26, 34], we did not model any time lag between onset of the intervention and dietary change.

The effects of the F&V incentive were derived from HIP, a randomized trial in SNAP participants that provided a 30% subsidy for F&V purchases [ 24, 25]. This effect size incorporated the average coverage and participant response to the program, its corresponding outreach and education, and potential shifts in spending between SNAP versus non-SNAP dollars ( S3 Text). For the combined incentive/disincentive program, this effect was extended to the other incentivized food groups, as this provided a more conservative effect size than larger estimated effects of price subsidies on healthful food items from a recent meta-analysis of interventional and prospective studies [ 26]. While SSB restriction would prohibit purchases using SNAP dollars, we recognized and accounted for the likely average response of individuals who might shift some of their spending on SSBs from SNAP dollars to other food dollars, as noted above. Accounting for each of these data sources and considerations, we estimated that a 30% F&V incentive in SNAP would increase overall intake of fruits by 23.4% and vegetables by 19.0%; an SSB restriction in SNAP would reduce overall intake of SSBs by 33.2%; a 30% incentive in SNAP for other healthful foods would increase their overall intake by between 19.0% and 24.2%; and a 30% disincentive in SNAP for SSBs, junk food, and processed meats would decrease their overall intake by between 13.3% and 17.4% ( S2 Table). S2 Table also describes several sensitivity analyses for these assumptions and corresponding calculations.

Effects of dietary changes on cardiometabolic risk

Our detailed methods for reviewing and synthesizing evidence to estimate effect sizes for associations between dietary factors and cardiometabolic endpoints have been reported [ 1, 3]. We utilized evidence from meta-analyses of prospective cohorts or randomized clinical trials evaluating direct associations of dietary factors with CHD, stroke, or type 2 diabetes, by age; associations of SSBs and added sugar from other foods with body mass index (BMI) and additional BMI-independent associations of SSBs with CHD, stroke, and diabetes, by age and overweight/obesity status; and effects of dietary sodium on blood pressure, by age, race, and hypertensive status (see S3 Table). To estimate cardiometabolic effects of junk food, we assessed the independent relationship between US junk food consumption and 13 other dietary components with established links to cardiometabolic risk ( S4 Table), such as fruits, vegetables, whole grains, nuts, omega-3 fats, potassium, red meat, processed meat, polyunsaturated fat as a replacement for carbohydrate or saturated fat, and SSBs. Using the identified 9 joint associations, some of which predicted harm and some of which predicted benefit, we estimated the etiologic effect of changes in junk food intake on cardiometabolic risk.

These estimated effects can model associations with cardiometabolic risk if there is not substantial bias from confounding (which might overestimate effects) or measurement error (which might underestimate effects). To reduce bias from confounding, all identified observational studies in the utilized meta-analyses included multivariable adjustment for other risk factors. Measurement error was generally not addressed, although some studies included serial dietary measures. We also recognized that associations of individual dietary components with health may differ from joint associations when foods are consumed as diet patterns: for example, due to clustering of healthful components such as fruits, vegetables, and whole grains, or unhealthful components such as SSBs and processed meats. We performed detailed validity analyses evaluating the extent to which estimated etiologic effects might be biased due to these limitations [ 1], including comparing estimated etiologic effects for individual dietary components to (1) observed associations of overall dietary patterns with clinical endpoints in long-term cohorts, (2) effects of dietary patterns on low-density lipoprotein cholesterol (LDL-cholesterol) and systolic blood pressure in randomized controlled feeding trials, and (3) effects of dietary patterns on hard CVD endpoints in a large randomized clinical trial ( S4 Text; S11- S13 Tables). Each of these validity analyses demonstrated that estimated etiologic effects for individual dietary components were very similar to what would be expected based on these other lines of evidence [ 1]. These observed etiologic effects also inherently account for the average dietary complements and substitutes in the population, as further described in S4 Text.

Microsimulation model structure and outputs

CVD-PREDICT is a validated discrete time microsimulation dynamic model, coded in C++, that simulates and quantifies the effects of policies on cardiometabolic outcomes including CHD, stroke, and type 2 diabetes, with annual updating of each health state ( S1 Text) [ 28, 29]. The model is run at the level of individuals, incorporating the probability of their health transitions according to each person's risk factors. Based on NHANES as described above, the model was populated with simulated SNAP participants and their corresponding risk factors including data on age, sex, systolic blood pressure, total cholesterol, HDL-cholesterol, BMI, smoking status, diabetes status, baseline dietary habits, and other characteristics as necessary. Each individual's risk was calculated annually, adapted primarily from the Framingham Risk Score, with calibration and validation to empirical historical disease trends [ 29] and with modeled transition health states including disease-free, death, and acute or chronic CHD events, stroke (cerebrovascular accident), and diabetes. CVD risk factors and subsequent estimated CVD incidence and mortality were extrapolated and updated using age and time trends from NHANES. At any given time point, a simulated individual could only be in 1 health state, with the probability of experiencing subsequent events based on individual characteristics and risk factors. The microsimulation process across each state and transitions are illustrated in Fig 1.

Model outputs included total and mean estimated outcomes for the simulated population over a 5-, 10-, and 20-year open cohort analytic period (2018-2038) as well as a closed cohort lifetime ( S1 Text). The specific model outcomes included deaths from CHD or stroke, nonfatal events including myocardial infarction, stroke, angina, resuscitated cardiac arrest, and type 2 diabetes, quality-adjusted life years (QALYs), and event-associated healthcare costs (see below).

Intervention and healthcare costs

Intervention costs were estimated including administrative costs of program implementation, EBT processing, retail infrastructure, outreach and education, and monitoring and evaluation; SNAP program subsidy costs for incentivized foods; and SNAP program savings for disincentivized foods (but not restricted foods, which produced no program savings) ( S5 Table). Administrative costs were derived from the HIP report estimates for extending the project nationally [ 25]. Because SNAP is an established national program with benefits operationalized through an existing electronic system at retailers that already discriminates products based on UPCs, retailers were considered unlikely to have any new meaningful costs due to implementation of the policies. The costs of food incentives and disincentives were calculated for each food category based on data from the USDA Economic Research Service Quarterly Food-at-Home Price Database [ 35], the estimated average national price of SSBs [ 36], the Consumer Expenditure Survey [ 37], and the USDA SNAP report [ 38].

As reported in detail elsewhere [ 29], direct healthcare costs were derived from various sources for all acute and chronic disease states, surgical procedures, screening, treatments, and side effects ( S6 Table). The total annual costs for individuals in each disease state were calculated using the weighted average of nonfatal and fatal states. The costs associated with chronic disease states were derived from various studies on costs for each individual disease state. Indirect costs such as lost productivity were conservatively excluded from the analysis.

Cost-effectiveness analysis

In accordance with recommendations from the Second Panel on Cost-Effectiveness in Health and Medicine [ 39], we conducted analyses from various perspectives. This included (1) a societal perspective, incorporating program administrative costs and healthcare costs but not costs or savings from direct food subsidies or disincentives (which can be considered an intra-societal cash transfer); (2) a government affordability perspective further incorporating SNAP program costs and savings from direct food subsidies and disincentives for all SNAP adults (age 35+ y); and (3) a government affordability perspective further incorporating SNAP program costs and savings from direct food subsidies and disincentives for all SNAP participants (half of whom are children, for whom our model estimated only costs, with no health benefits or healthcare cost-savings). Each government affordability perspective can also be considered as a modified societal perspective that includes predominantly government healthcare and SNAP costs, while also adding the considerable government costs of food subsidies-an intra-societal transfer to the public not typically included in a societal perspective-and also intervention and health costs borne by other health payers and consumers.

All costs were inflated to constant 2017 US dollars, and all costs and health endpoints discounted annually by 3%. Net costs were calculated as intervention costs minus healthcare cost-savings from reduced cardiometabolic diseases. Incremental cost-effectiveness ratios (ICERs) were calculated as the net change in costs divided by the net change in QALYs. Willingness-to-pay thresholds were evaluated at $150,000 and $50,000 per QALY, consistent with American Heart Association/American College of Cardiology recommendations [ 40].

Probabilistic sensitivity analyses

To assess the potential impact of uncertainty in key inputs, we performed probabilistic sensitivity analyses jointly incorporating the uncertainty distributions of multiple parameters. This uncertainty included uncertainty in policy effect sizes, diet-disease relative risks (including their variation by age), CVD risks, implementation costs, food units costs, formal and informal healthcare costs, and utility weights ( S9 Table). One thousand simulations were run drawing from the uncertainty distributions of each of these inputs at 5 years and over a lifetime, with 95% uncertainty intervals (UIs) based on the 2.5th and 97.5th percentiles of the 1,000 simulations.

Results

Population characteristics and dietary intakes

The mean (SD) age of adult SNAP participants was 52.1 (12.3) years, about half (50.8%) were white, and more than 1 in 3 participants (35.0%) had some college education or more ( Table 1). Among adults on SNAP, 22.4% were on Medicare, 29.5% on Medicaid, and 41.7% on either Medicare or Medicaid (with 10.2% on both, i.e., dual-eligible). Considering adults on Medicare or Medicaid as the denominator, 12.7% of Medicare and 61.1% of Medicaid beneficiaries were also on SNAP.

The current baseline consumption levels and estimated changes in consumption with the policy interventions are shown in Table 2. The F&V subsidy would increase intakes of fruits by 19.1 g/d and vegetables by 25.6 g/d. Adding an SSB restriction would lower SSB intake by 139 g/d (4.7 fl oz/d). A combined incentive/disincentive program would have a smaller effect on SSBs (−1.7 fl oz/d), but would also modestly increase intakes of whole grains, fish, and plant-based oils and modestly reduce intakes of processed meats and junk food. To place these changes in perspective as a proportion of the 2015 Dietary Guidelines for Americans recommendations [ 41] (for a 2,000 kcal/d diet, the following equivalents: fruits, 2 cups/d; vegetables, 2.5 cups/d; nuts, 5 oz/wk; whole grains, 3 oz/d; and fish, 8 oz/wk), the changes in intake with SNAP-plus would achieve 9.6% of the recommendations for fruits, 11.5% for vegetables, 8.5% for nuts, 4.0% for whole grains, and 12.1% for fish. Such comparisons were not made for unhealthy food items like SSBs, where the optimal intake could be considered 0.

Health outcomes

Health gains, healthcare cost-savings, intervention costs, and ICERs are summarized in Table 3, with results of probabilistic sensitivity analyses in S10 Table. At 5 years, our simulation estimated that the F&V incentive would prevent 38,782 CVD events, with minimal effects on diabetes; the F&V incentive/SSB restriction policy would prevent 93,933 CVD events and 30,443 cases of diabetes; and SNAP-plus would prevent 116,875 CVD events and 26,138 cases of diabetes. Over a lifetime, corresponding values were 303,911, 797,888, and 939,965 CVD events prevented and −1,077, 171,357, and 146,590 cases of diabetes prevented, with respective healthcare cost-savings of $6.77 billion, $39.16 billion, and $41.93 billion.

Cost-effectiveness

From a societal perspective, considering both program implementation costs and healthcare savings, all 3 interventions were cost-saving at 5 years and beyond, with estimated lifetime societal savings of $6.69 billion, $39.05 billion, and $41.82 billion, respectively ( Table 3; S1 Fig). From a government affordability perspective, incorporating the government costs of providing food subsidies, the F&V incentive was of low cost-effectiveness at 5 years, with ICERs of $204,928/QALY for subsidizing SNAP adults and $548,053/QALY for subsidizing all SNAP participants. However, by 10 years, the F&V incentive achieved traditional cost-effectiveness (<$150,000/QALY) for subsidizing SNAP adults, and over a lifetime, was very cost-effective (<$50,000/QALY) for subsidizing SNAP adults and cost-effective (<$150,000/QALY) for subsidizing all SNAP participants.

Adding SSB restriction produced a modest increase in policy administrative costs and (due to improved survival) increased F&V subsidy costs, but also a 4- to 5-fold increase in healthcare cost-savings ( Table 3; S1 Fig). Consequently, from a government affordability perspective, the intervention was very cost-effective at 5 years and cost-saving at 10+ years for subsidizing SNAP adults (lifetime savings: $17.17 billion), and cost-effective at 10 years and very cost-effective at 20+ years, even when subsidizing all SNAP participants (lifetime ICER: $5,216/QALY).

For the SNAP-plus intervention, even with several more categories of incentivized foods, the food disincentives funded the incentives, creating a net program food cost-savings, or revenue. From a government affordability perspective, this resulted in a cost-saving intervention at all time points for subsidizing SNAP adults (lifetime savings: $27.74 billion) and for subsidizing all SNAP participants (lifetime savings: $63.33 billion). Evaluating costs within SNAP alone (i.e., ignoring healthcare savings), the SNAP-plus intervention remained cost-saving. For example, at 5 years, $0.15 billion in administrative costs was offset by 4.93 billion in savings from the combined food incentives/disincentives.

Stratified and sensitivity analyses

The cost-effectiveness or cost-savings and relative health gains of each intervention were generally similar across groups stratified by age, sex, race/ethnicity, education, and insurance status (Figs 2- 4; S8 Table). For example, lifetime ICERs for the F&V incentive ranged from a low of $50,867/QALY in men to a high of $78,688/QALY in women from a government affordability perspective (subsidizing all SNAP participants). Adding SSB restriction substantially lowered ICERs in all groups, and produced net cost-savings in young adults (age 35-44 y) and in men. SNAP-plus was cost-saving in essentially all subgroups and for all perspectives, except for among college graduates from a government affordability perspective (subsidizing all SNAP participants), in whom the lifetime ICER was still very cost-effective, at $7,878/QALY.

In probabilistic sensitivity analyses, SNAP-plus was cost-saving both at 5 years and over a lifetime in 1,000 of 1,000 simulations (100%) from both societal and government affordability perspectives ( Fig 5; S10 Table). The F&V incentive and F&V incentive/SSB restriction policies were also each cost-saving in 100% of simulations both at 5 years and over a lifetime from the societal perspective. From a government affordability perspective incorporating food subsidy costs for all SNAP participants including children and adults aged <35 years, the F&V incentive did not achieve cost-effectiveness (<$150,000/QALY) in any simulation at 5 years, but achieved cost-effectiveness in 99.9% of simulations over a lifetime, while the F&V incentive/SSB restriction policy was cost-saving or cost-effective in 44.6% of simulations at 5 years, and 100% over a lifetime.

Additional sensitivity analyses are shown in S7 Table. For the F&V incentive/SSB restriction policy, altering the assumed proportion of SSB purchases that would shift from SNAP dollars to other food dollars did not greatly alter the inference on this intervention. Similarly, for SNAP-plus, assuming a larger shift in purchases of disincentivized foods from SNAP dollars to other food dollars retained cost-savings at all time points for all perspectives. Removing subsidies in SNAP-plus for fish and plant-based oils, the most costly items, resulted in even larger cost-savings, with $80.77 billion in lifetime savings from a government affordability perspective (subsidizing all SNAP participants).

Discussion

Based on our microsimulation model and nationally representative data, a F&V incentive, F&V incentive with SSB restriction, and combined incentive/disincentive program for multiple foods within SNAP would each generate significant health gains among low-income Americans. Our simulation estimated that the F&V subsidy would gain about 650,000 QALYs, while the other interventions would gain about 2.1 and 2.5 million QALYs. All interventions were cost-saving from a societal perspective, with estimated lifetime societal savings of about $6.7 billion, $39 billion, and $42 billion, respectively. From a government affordability perspective, all 3 programs met traditional cost-effectiveness thresholds over a lifetime, with superior cost-effectiveness when SSB restrictions were added to the F&V incentive, and net government cost-savings with SNAP-plus. Notably, the most conservative government affordability perspective incorporated the intervention and food subsidy costs for all SNAP participants, including children and young adults, while estimating health and healthcare cost benefits only for adults aged 35 years and older. Because consuming healthier diets at younger ages is likely to lead to health benefits, the overall health gains and cost-effectiveness or cost-savings of each intervention are likely to be underestimated.

The current average SNAP benefit is $126 per person per month, or about $4.20 per day [ 7]. Disparities in diet by SNAP status persist [ 12]. In 2015, the USDA launched the SNAP Food Insecurity Nutrition Incentive grant program, providing up to $31.5 million over 2 years for point-of-purchase incentives to increase F&V purchases [ 43]. Although this represents an important new commitment to leveraging SNAP for healthier choices and better well-being, our analysis suggests this amount-less than 50 cents per SNAP participant per year-is a small fraction of the approximately $2.6 billion/year needed to provide a 30% F&V incentive to all SNAP participants. In the current era of budget constraints, achieving political consensus for such an increase may be challenging. Incorporation of the associated health gains and healthcare cost-savings, as in our analysis, provides new evidence to inform this discussion. From a societal perspective, such an intervention was estimated to prevent tens of thousands of CVD events, gain substantial QALYs, and be cost-saving. From a government affordability perspective, a universal 30% F&V incentive in SNAP became cost-effective long term, although the shorter-term costs per QALY may not be attractive for resource-constrained decisions on short-term policy priorities.

Our findings suggest further health gains, cost-savings, and cost-effectiveness of adding SSB restriction to the F&V incentive in SNAP. While several groups have recommended SSB restrictions in SNAP [ 18, 20, 22, 23], others have opposed such a change. In 2015, the bipartisan National Commission on Hunger recommended removing SSBs from the list of eligible purchases in SNAP [ 23]. Both the technology and precedent exist for altering product eligibility (e.g., alcohol and pre-prepared hot foods cannot be purchased in SNAP), and several states including California, Florida, Missouri, Wisconsin, and Texas have considered pilot programs to test restriction of SSBs [ 44]. However, such programs have never yet been federally authorized. SSB manufacturers oppose such a restriction [ 45], which appears to unfairly single out one product as compared to candy or other junk food, for example. Some anti-poverty advocates and ethical scholars argue that such a restriction is paternalistic, creating "a public policy message that poor people require government intervention to manage their food choices whereas higher-income persons do not" [ 44]. Our findings provide new data in this regard, estimating that adding SSB restriction to the F&V incentive would prevent an additional 494,000 CVD events and gain 1.46 million more QALYs among low-income Americans on SNAP. Government affordability also improved to about $5,000/QALY, superior to many other federally approved health interventions including drug treatment of hypertension ($20,000/QALY [ 46]), use of statins for primary prevention ($37,000/QALY [ 28]), and adding PCSK9 inhibitors to statins in patients with CVD ($414,000/QALY) or heterozygous familial hypercholesterolemia ($503,000/QALY) [ 47].

Our results suggest that a combined food incentive/disincentive program may be most attractive. The F&V incentive has widespread appeal yet was costly, while SSB restriction produced additional important health gains and cost-savings but raises political challenges in isolating a single product category and restricting choice. In comparison, the SNAP-plus program incentivized a broad range of foods, with partial disincentives but not absolute restrictions on less healthy products, preserving participant choice. The bipartisan National Commission on Hunger recommended incentivizing F&V, whole grains, high-quality proteins, and other healthful foods in SNAP [ 23]. Our results indicate that adding disincentives greatly augments both the health gains and affordability of such an intervention. Overall, the SNAP-plus intervention produced the largest gains in health and healthcare savings, and was cost-saving at all time points from all perspectives. Omitting healthcare savings and considering SNAP fiscal integrity alone-e.g., such as might be done by the Congressional Budget Office-the SNAP-plus intervention resulted in net savings of $4.78 billion at 5 years and $21.19 billion over a lifetime, funds that could be reinvested in SNAP to maintain or expand overall benefits. Novel online and technology-based financial incentive and education programs for healthier food purchases are now available for higher-income Americans through worksite wellness, health insurance, and life insurance programs [ 32]. Such technologies could be leveraged to provide SNAP participants with education and combined financial incentives and disincentives for healthier eating, with evaluation in state pilot programs.

Our investigation has several strengths. We utilized a validated microsimulation model and national data, increasing confidence in the validity and generalizability of our estimates. We accounted for proportions of food purchased from SNAP-eligible venues (as opposed to restaurants, cafeterias, etc.), proportions of purchases using SNAP versus other food dollars, and potential shifts in spending between SNAP and other food dollars, each reducing the overall effect and providing more appropriate and conservative estimates of potential impact. The potential effects of dietary substitutes and complements were also considered because the diet-disease etiologic effects (relative risks) for changes in the targeted dietary factors were based on long-term prospective cohort studies, which implicitly incorporate additional health effects of the average dietary substitutes and complements across the population. Policy intervention effects were based on trials among SNAP participants or, when these not available, prospective interventions and studies in general populations. We assessed both shorter- and longer-term health impacts, costs, and cost-effectiveness, providing a range of results across different potential time periods of interest.

Potential limitations should be considered. Our model cannot prove the health and cost impacts of national SNAP interventions. Rather, the estimates provide evidence that can be considered and incorporated into the design of state waiver programs for evaluation. Although prior interventions observed little shifting of spending dollars to maximize incentives [ 33], we cannot exclude that, over many years, participants might shift more of their spending on disincentivized foods to other food dollars, reducing intervention efficacy and cost-effectiveness over time. Conversely, establishing incentives and disincentives on specific food categories could also shift long-term knowledge and social norms around food purchases, increasing the efficacy and cost-effectiveness of the intervention over time. While our estimated etiologic effects for dietary changes inherently incorporated the average dietary complements and substitutes in the population, health gains could be augmented by additional intervention components aiming to encourage or discourage specific complements and substitutes. Our CVD risk calculations incorporated differences between people in age, sex, systolic blood pressure, total cholesterol, HDL-cholesterol, smoking status, diabetes status, baseline diet, and dietary changes due to the intervention, but not race/ethnicity. However, the risk factors in our model capture many of the major determinants (or further correlate with many of the determinants) that explain racial/ethnic disparities in disease risk, especially when further restricted by income (SNAP eligibility) as in our analysis. Our comparative effectiveness framework also minimizes the influence of potential residual heterogeneity in CVD risk, as uncaptured factors would influence both each intervention scenario and the base case scenario, having relatively small effects on the incremental comparison. We did not incorporate potential additional benefits of synchronizing use of SNAP-Ed, an existing $400 million/year educational program, with these financial incentive/disincentive programs. We conservatively excluded indirect costs of disease states, such as estimated losses in productivity, which, if included, would make each of the interventions more cost-effective or cost-saving.

In conclusion, our modeling estimates that leveraging SNAP for improved nutrition through financial incentives for healthier foods, and disincentives or altered eligibility for unhealthy foods, would generate significant health gains and healthcare cost-savings.

Supporting information

S1 Fig. Summary of health and cost-effectiveness findings for 3 dietary policy interventions in SNAP.

A 30% F&V incentive, a 30% F&V incentive with SSB restriction, and a combined 30% incentive/disincentive program for multiple foods that preserves choice (SNAP-plus). Findings are shown at 5 years, 10 years, and 20 years and over a lifetime.
https://doi.org/10.1371/journal.pmed.1002661.s001
(TIF)

Acknowledgments

The authors thank all of the collaborators and advisory groups in the Food-PRICE project ( https://www.food-price.org).

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Cost-effectiveness of financial incentives and disincentives for improving food purchases and health through the US Supplemental Nutrition Assistance Program (SNAP): A microsimulation study

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Dariush Mozaffarian, Junxiu Liu, Stephen Sy, Yue Huang, Colin Rehm, Yujin Lee, Parke Wilde, Shafika Abrahams-Gessel, Thiago de Souza Veiga Jardim, Tom Gaziano has COMPANION In an unhealthy food system, what role should SNAP play? Subject Areas For more information about PLOS Subject Areas, click here. We want your feedback.

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Background

Diet-related environmental and policy interventions are being advocated at a population level because individual change is more likely to be facilitated and sustained if the environment within which choices are made supports healthful food options.

Purpose

This study aims to review research that examines factors having an influence on food choices in social environments, physical environments, and macroenvironments.

Methods

A snowball strategy was used to identify relevant peer-reviewed studies and reviews, with a focus on research completed in the US and published within the past 10 years.

Results

Research has identified a number of environmental factors associated with dietary intake; however, the majority of completed studies have methodological limitations which limit their credibility to guide interventions and policy changes.

Conclusions

Future research will need to emphasize multilevel investigations, examine how associations vary across population subgroups, develop a standard set of measures for assessing food environments and policies, and improve dietary assessment methodology.

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A Review of Environmental Influences on Food Choices - Annals of Behavioral Medicine

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Background Diet-related environmental and policy interventions are being advocated at a population level because individual change is more likely to be facilitated and sustained if the environment within which choices are made supports healthful food options. Purpose This study aims to review research that examines factors having an influence on food choices in social environments, physical environments, and macroenvironments.

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The Cost of Satisfying Fruit andVegetable Recommendations in theDietary Guidelines

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The Cost of Satisfying Fruit andVegetable Recommendations in theDietary Guidelines

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"We also find that a family of four could purchase a sufficient variety of fruits and vegetables to meet those same guidelines with a limited budget, based on the U.S. Department of Agriculture’s Thrifty Food Plan (TFP). However, this would require the household to allocate a much larger share of its overall food budget to fruits and vegetables and a smaller share to foods high in solid fats, added sugars, and sodium."

Angela: This is the most important resource used in the project because it identifies the reason why fruits and veggies are not expensive and budget-friendly.

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The Cost of Satisfying Fruit and Vegetable Recommendations in the Dietary Guidelines

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The dynamics of food shopping behavior:Exploring travel patterns in low-income Detroit neighborhoods experiencing extreme disinvestment using agent-based modeling

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The dynamics of food shopping behavior:Exploring travel patterns in low-income Detroit neighborhoods experiencing extreme disinvestment using agent-based modeling

# data[].attributes.body
Vojnovic, Ligmann-Zielinska, A., & LeDoux, T. F. (2020). The dynamics of food shopping behavior: Exploring travel patterns in low-income Detroit neighborhoods experiencing extreme disinvestment using agent-based modeling. PloS One, 15(12), e0243501–. https://doi.org/10.1371/journal.pone.0243501

# data[].attributes.attachment_link.title
 The dynamics of food shopping behavior- Exploring travel patterns in low-income Detroit neighborhoods experiencing extreme disinvestment using agent-based modeling

# data[].attributes.headline
Does urban form influence grocery shopping frequency?A study from Seattle,Washington, USA

# data[].attributes.subject
Does urban form influence grocery shopping frequency?A study from Seattle,Washington, USA

# data[].attributes.body
Purpose: "The purpose of this paper is to ascertain how elements of the built environment may or may not influence the frequency of grocery shopping."

Jiao, Vernez Moudon, A., & Drewnowski, A. (2016). Does urban form influence grocery shopping frequency? A study from Seattle, Washington, USA. International Journal of Retail & Distribution Management, 44(9), 903–922. https://doi.org/10.1108/IJRDM-06-2015-0091

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Does urban form influence grocery shopping frequency? A study from Seattle, Washington, USA

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Going outside the neighborhood: The shopping patterns and adaptations of disadvantaged consumers living in the lower eastside neighborhoods of Detroit, Michigan

# data[].attributes.subject
Going outside the neighborhood: The shopping patterns and adaptations of disadvantaged consumers living in the lower eastside neighborhoods of Detroit, Michigan

# data[].attributes.body
LeDoux, & Vojnovic, I. (2013). Going outside the neighborhood: The shopping patterns and adaptations of disadvantaged consumers living in the lower eastside neighborhoods of Detroit, Michigan. Health & Place, 19, 1–14. https://doi.org/10.1016/j.healthplace.2012.09.010

# data[].attributes.attachment_link.title
Going outside the neighborhood- The shopping patterns and adaptations of disadvantaged consumers living in the lower eastside neighborhoods of Detroit, Michigan

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Mothers’ self-reported grocery shopping behaviours withtheir 2- to 7-year-old children: relationship between feedingpractices and mothers’ willingness to purchase child-requestednutrient-poor, marketed foods, and fruits and vegetables

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Mothers’ self-reported grocery shopping behaviours withtheir 2- to 7-year-old children: relationship between feedingpractices and mothers’ willingness to purchase child-requestednutrient-poor, marketed foods, and fruits and vegetables

# data[].attributes.body
Lively, Babawale, O., Thompson, D. M., Morris, A. S., Harris, J. L., Sisson, S. B., Cheney, M. K., & Lora, K. R. (2017). Mothers’ self-reported grocery shopping behaviours with their 2- to 7-year-old children: relationship between feeding practices and mothers’ willingness to purchase child-requested nutrient-poor, marketed foods, and fruits and vegetables. Public Health Nutrition, 20(18), 3343–3348. https://doi.org/10.1017/S1368980017002142

Objective: "To assess relationships between mothers’ feeding practices (food as a reward, food for emotion regulation, modeling of healthy eating) and mothers’ willingness to purchase child-marketed foods and fruits/vegetables (F&V)
requested by their children during grocery co-shopping."

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Mothers’ self-reported grocery shopping behaviours with their 2- to 7-year-old children- relationship between feeding practices and mothers’ willingness to purchase child-requested nutrient-poor, marketed foods, and fruits and vegetables

# data[].attributes.headline
US Household Food ShoppingPatterns: Dynamic Shifts Since2000 And SocioeconomicPredictors

# data[].attributes.subject
US Household Food ShoppingPatterns: Dynamic Shifts Since2000 And SocioeconomicPredictors

# data[].attributes.body
Stern, Robinson, W. R., Ng, S. W., Gordon-Larsen, P., & Popkin, B. M. (2015). US Household Food Shopping Patterns: Dynamic Shifts Since 2000 And Socioeconomic Predictors. Health Affairs Web Exclusive, 34(11), 1840–1848. https://doi.org/10.1377/hlthaff.2015.0449

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US Household Food Shopping Patterns- Dynamic Shifts Since 2000 And Socioeconomic Predictors

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General and food-selection specific parenting style in relation to the healthfulness of parent-child choices while grocery shopping

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General and food-selection specific parenting style in relation to the healthfulness of parent-child choices while grocery shopping

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Lucas-Thompson, Graham, D. J., Ullrich, E., & MacPhee, D. (2017). General and food-selection specific parenting style in relation to the healthfulness of parent-child choices while grocery shopping. Appetite, 108, 353–360. https://doi.org/10.1016/j.appet.2016.10.019

Objective: investigate links between general parenting style, parental warmth and limit setting (important dimensions of parenting style) during grocery shopping, and the healthfulness of foods chosen

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General and food-selection specific parenting style in relation to the healthfulness of parent-child choices while grocery shopping

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Availability of affordable healthy food in Hillsborough County, Florida

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Availability of affordable healthy food in Hillsborough County, Florida

# data[].attributes.body
Borja, & Dieringer, S. (2019). Availability of affordable healthy food in Hillsborough County, Florida. Journal of Public Affairs, 19(3), e1866–n/a. https://doi.org/10.1002/pa.1866

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Availability of affordable healthy food in Hillsborough County, Florida

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The Nutrient Rich Foods Index helps to identify healthy, affordable foods

# data[].attributes.subject
The Nutrient Rich Foods Index helps to identify healthy, affordable foods

# data[].attributes.body
DREWNOWSKI. (2010). The Nutrient Rich Foods Index helps to identify healthy, affordable foods. The American Journal of Clinical Nutrition, 91(4), 1095S–1101S. https://doi.org/10.3945/ajcn.2010.28450D

Objective of study: "Our aim was to identify healthy, affordable foods and
food groups by using the NRF index and US Department of Agriculture (USDA) nutrient composition and food prices data sets."

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Nutrient Rich Foods Index addordable foods

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Abstract

Background: US fruit and vegetable (FV) intake remains below recommendations, particularly for low-income populations. Evidence on effectiveness of rebates in addressing this shortfall is limited.

Objective: This study evaluated the USDA Healthy Incentives Pilot (HIP), which offered rebates to Supplemental Nutrition Assistance Program (SNAP) participants for purchasing targeted FVs (TFVs).

Design: As part of a randomized controlled trial in Hampden County, Massachusetts, 7500 randomly selected SNAP households received a 30% rebate on TFVs purchased with SNAP benefits. The remaining 47,595 SNAP households in the county received usual benefits. Adults in 5076 HIP and non-HIP households were randomly sampled for telephone surveys, including 24-h dietary recall interviews. Surveys were conducted at baseline (1-3 mo before implementation) and in 2 follow-up rounds (4-6 mo and 9-11 mo after implementation). 2784 adults (1388 HIP, 1396 non-HIP) completed baseline interviews; data were analyzed for 2009 adults (72%) who also completed ≥1 follow-up interview.

Results: Regression-adjusted mean TFV intake at follow-up was 0.24 cup-equivalents/d (95% CI: 0.13, 0.34 cup-equivalents/d) higher among HIP participants. Across all fruit and vegetables (AFVs), regression-adjusted mean intake was 0.32 cup-equivalents/d (95% CI: 0.17, 0.48 cup-equivalents/d) higher among HIP participants. The AFV-TFV difference was explained by greater intake of 100% fruit juice (0.10 cup-equivalents/d; 95% CI: 0.02, 0.17 cup-equivalents/d); juice purchases did not earn the HIP rebate. Refined grain intake was 0.43 ounce-equivalents/d lower (95% CI: -0.69, -0.16 ounce-equivalents/d) among HIP participants, possibly indicating substitution effects. Increased AFV intake and decreased refined grain intake contributed to higher Healthy Eating Index-2010 scores among HIP participants (4.7 points; 95% CI: 2.4, 7.1 points).

Conclusions: The HIP significantly increased FV intake among SNAP participants, closing ∼20% of the gap relative to recommendations and increasing dietary quality. More research on mechanisms of action is warranted. The HIP trial was registered at clinicaltrials.gov as NCT02651064.

Keywords: Healthy Incentives Pilot; Supplemental Nutrition Assistance Program; financial incentives; fruits and vegetables; randomized controlled trial.

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national center for biotechnology information

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Financial incentives increase fruit and vegetable intake among Supplemental Nutrition Assistance Program participants: a randomized controlled trial of the USDA Healthy Incentives Pilot - PubMed

# data[].attributes.attachment_link.description
Background: US fruit and vegetable (FV) intake remains below recommendations, particularly for low-income populations. Evidence on effectiveness of rebates in addressing this shortfall is limited. Objective: This study evaluated the USDA Healthy Incentives Pilot (HIP), which offered rebates to Supplemental Nutrition Assistance Program (SNAP) participants for purchasing targeted FVs (TFVs).

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The objective of this study was to create/test a social marketing campaign to increase fruit/vegetable (FV) intake within Oregon Supplemental Nutrition Assistance Program (SNAP) eligible families. Focus groups ( n = 2) and pre/post campaign phone surveys ( n = 2082) were conducted in intervention counties (IC) and one control county. Participants were female (86%-100%) with 1-2 children at home. Mean FV intake/without juice was 3.1 servings/day; >50% preferred the Internet for delivery of healthy eating information. Participants reported time/financial burdens, low household FV variety and desirability of frozen/canned FV, and acceptance of positive messages. A Food Hero (FH) campaign was created/delivered daily August-October 2009 to mothers through multiple channels (e.g., grocery stores, online, educators). Results showed that the IC had better FH name recall (12%) and interpretation of intended messages (60%) vs. control (3%, 23%, respectively). Compared to controls, the IC were less likely to report healthy food preparation as time consuming or a FV rich diet expensive, and it was easier to get their family to eat fruit. Results did not vary based on county/household characteristics. The FH campaign increased FH awareness and positive FV beliefs. A longer campaign with FV assessments will increase understanding of the target audience, and allow for campaign refinement.

Keywords: low-income women, focus group, survey, nutrition, social media, Supplemental Nutrition Assistance Program (SNAP), audience-centered positive messaging, health behavior messages, canned, frozen

2.1. Study Overview

Two contractors were hired to add expertise to the campaign development team, a business research firm and a SM firm. The research firm ( Close to the Customer Project, OSU's School of Business) assisted with writing research phone surveys (PS) and focus group (FG) questions, managed all phases of participant recruitment, lead the FGs, produced verbatim transcripts of FG discussions and analyzed FG data for common themes, conducted PSs, analyzed the data, and created data summary reports. The SM firm ( EnviroMedia, Portland, OR, USA) assisted with brand creation, campaign message development, project management, and gave input into PS and FG questions. The campaign development included the eight elements of the SM benchmark criteria [ 17]. OSU's nutrition researchers gave input on all steps of the project ().

All research was conducted with SNAP participants in select Oregon counties. The timeline and steps of the research and campaign development are outlined in . Pre-campaign research included conducting FGs (Step 1: FG-1; n = 25 participants), which were used to help design a pre-campaign PS, including FV belief and barrier questions (Step 2: PS-1; n = 1244 participants). The PS's also included the validated BRFSS FV questions to assess FV intake [ 21]. In addition, the FGs provided information related to health priorities, beliefs and experiences. Using the results from FG-1 and PS-1, Step 3 developed the campaign, including creating draft campaign names, logos, messages and delivery channels ( and ). Key components of the SCT aligned with the FG-1 and PS-1 results; thus, the SCT was used to inform campaign development/implementation (Steps 3-6). As second set of FGs (Step 4: FG-2, n = 11) were used to test messages and components of the developed campaign. In Step 5, final campaign materials and delivery channels were determined/created and then implemented in Step 6. The campaign was delivered through multiple channels, including a web site, direct mail, billboards, web banner ads, grocery store demonstrations, grocery cart ads, and county SNAP-Ed educators delivering the campaign using Food Hero Community Kits [ 22]. The community kits were designed to provide locally adaptable campaign tools and materials for OSU's Extension county educators and their partners. The goal of the community kit was to assure that comprehensive educator/partner Food Hero promotion occurred concurrently with other campaign communication channels. Public relations efforts throughout the campaign included television and radio interviews, a family video makeover contest, and social media postings (i.e., YouTube and Facebook). The Food Hero social media project is described elsewhere [ 23]. Due to the time left in the project funding cycle, the campaign could only run for 2-months. Near the end of the campaign, a second PS was conducted to test for campaign awareness and FV beliefs, and to gain further insights for future campaign development (Step 7: PS-2; n = 802).

Table 1

Message Priority Given by Participants from Highest (1) to Lowest (7) Preference

Messages Tested in Focus Groups 2 (FG-2)

1. Canned, frozen, or fresh they all start out the same.

2. Kids would pick candy for every meal, good thing you're in charge.

3. Canned and frozen fruits and vegetables make your money go further.

4. Buying canned and frozen helps you get more for less.

5. An apple a day is not that far away.

6. Fruits and vegetables are within reach.

7. In these tough economic times get more of a good thing.

Messages Used in Campaign and then Tested in Phone Survey 2 (PS-2)

1. Give them more of the good stuff (direct mail and billboards)

2. Brighten your plate (website banners, refrigerator magnet grocery store reinforcement).

2.2. Study Design

From Oregon's 36 counties, four (rural = 2; metropolitan = 2) were selected for data collection and to receive the Food Hero campaign (). Inclusion criteria for all groups included current SNAP-Ed series of adult/family classes at multiple sites, availability of multiple media buy options, adequate population base for data collection, and SNAP staff available for assistance. Due to cost constraints, only one control county (Benton) was selected for PS-2 data collection because it had qualities of Oregon's rural and metropolitan counties (i.e., mid-sized population, ethnic diversity, away from a major highway). Benton county residents were also less likely to see Food Hero campaign billboards because it is located off Oregon's only major highway.

2.3. Participants and Recruitment

All FG and PS participants were randomly selected from a list of all English speaking SNAP families living in the targeted counties. The Oregon Department of Human Services (DHS) provided the names and phone numbers, either landlines or cell. For PS-1 it was determined that 250 responses were needed per county, which allowed for counties to be analyzed by themselves with a margin of error ±5% on dichotomous questions (). Oversampling was done to assure adequate sample size. Calls were made and consenting participants self-verified that they met the following inclusion criteria; SNAP enrollment, county residency, being the primary household food preparer, and having one or more children <18 years living in their home.

Table 2

Rural versus urban responses from FG-1 and PS-1 were similar, thus, FG-2 was conducted in only one county (Marion) (). Participants in FG-1 and PS-1 were 86%-92% female, thus, only females were recruited for FG-2. Due to DHS privacy requirements we do not know if participants overlap between or within the FG and PS samples, but FG participants represented less than 2% of the total study sample. Rural and urban responses on PS-1 were similar; therefore, the sample size for PS-2 was decreased. The research was approved by the Oregon State University Institutional Review Board and participants gave informed consent.

2.4. Instruments and Procedures

2.4.1. Focus Groups

For each FG 10-12 participants were recruited via phone calls, then mailed information packets, and received a reminder called prior to the meeting. Compensation was given for the 2-h FG meetings (FG-1: meal +$50; FG-2: meal +$25). Close to the Customer Project provided trained facilitators who lead participants through a series of pre-determined questions designed to elicit information regarding FV intake by the family, beliefs regarding FV intake, and barriers to FV intake. Questions for FG-1 focused on campaign development, while questions for FG-2 focused on testing potential campaign messages and components. Follow-up and probing questions were used as necessary, and participants engaged in writing responses and small group discussion.

2.4.2. Phone Surveys

PS-1 data were collected early in the pre-campaign, while PS-2 was conducted the last 2-weeks of the campaign (). Both PSs used identical recruitment procedures. Due to DHS privacy requirements, all potential participants received a letter from DHS with the following information: (1) the purpose and time commitment of the survey; (2) that DHS had given their phone number to OSU; and (3) they might receive a phone call asking them to participate in the survey. Trained PS interviewers conducted the surveys, which included moving systematically through a series of questions (PS-1: 70 questions, 24% response rate, interview time ~10 min; PS-2: 32 questions, 11% response rate, interview time was ~5.5 min). If the call resulted in no answer/answering machine, four more call attempts were made before discarding the telephone number. Participants received no compensation. Using mock phone interviews, interviewers practiced and PSs were pre-tested for length, time and clarity. No changes were made to the survey based on the mock interviews. Rural counties were disproportionately oversampled to assure sufficient responses.

2.5. Data Analysis

Analysis of FGs was iterative and occurred at several levels by the same four trained researchers who attended all FGs. The research team debriefed immediately following each FG, identifying key insights, themes, and unusual findings. Next the FGs were transcribed and transcripts were read individually by each of the researchers who coded and identified emerging themes. Researchers then met as a group to agree upon and define themes. Once themes were identified, researchers identified quotes and organized them according to thematic content. The focus groups were "exploratory" in nature and the insights gained from the groups informed the construction of the PS questionnaires.

For the PSs, summary statistics were generated for demographic data. ANOVA was used to determine if differences existed between PS-1 and PS-2 and PS-2 and control PS-2. Results showed no differences between surveys based on demographic data (county of residence, single/dual parent household, household size); thus, data from the intervention counties were combined for further analysis. Independent t-tests were then used to compare changes in variables/belief statements pre/post campaign using predetermined hypotheses based on campaign expectations.

Table 3

3.1. Focus Groups and Phone Survey 1

Step two of the SM benchmark criteria states that a strong understanding of the audience should be developed by combining data from different sources; thus, results from FGs and PS-1 were combined into themes and discussed below. FG and PS-1 results were grouped into five themes/outcomes: (1) audience demographics and characteristics; (2) self-reported FV intake and consumption; (3) grocery shopping and meal preparation habits; (4) preferred communication channels; and (5) responses to previously developed campaigns and messages.

Main Themes

Theme 1: Audience demographics and characteristics.

Overall, the participants were female (86%-100%), mean age range 34-43 years, single parents (39%), working outside the home at least half-time (33%-36%) and had 3-4 people in the household (36%-65%), including 1-2 children (64%-76%). PS-1 results showed that single adult households faced additional time and money constraints compared to two adult households. One FG participant said, "Money, I worry about money, I worry about it all the time, it's a big concern so I try to keep it from my kid so she doesn't see it." Working and stay-at-home parents also expressed time constraints.

Theme 2: Self-reported fruit and vegetable intake and consumption.

Based on PS-1, the mean servings/day of vegetables was 1.7, while the mean serving/day for fruit was 3.1 with juice and 1.4 serving/day without juice. Most PS-1 participants (70%) self-reported being knowledgeable about preparing vegetables and found it easy for their families to eat enough vegetables (60%) and fruit (80%). FG results were similar and responses from the FGs are included below:

"Potatoes are not just for dinner, we can have them for breakfast and lunch."

"Carrots are great. I have them as a snack."

"They are a good snack."

"My kids like the baby carrots so instead of buying the big long bugs bunny carrots they'll eat the baby carrots."

"I always have them [bananas] in my house. They are a year round fruit for the most part. When they get home from school they have one."

When shopping for groceries, PS-1 participates reported that "low price" was "very important" (67%), while "fast to prepare" (14%) and "easy to prepare" (15%) were less important. Over half (51%) of the PS-1 participants agreed that a diet rich in FVs was expensive. FGs participants often mentioned price as being important when shopping, "I only buy cereal when it is on sale or with a coupon." Being fast to prepare was also mentioned, but less often, "We eat pasta 'cause it's fast."

Participants from PS-1 and FGs reported that fresh produce was the preferred choice for being most nutritious (i.e., not containing extra sugar/salt or losing nutritional value from canning or freezing), while frozen foods were a good second choice, and canned were less preferred. In PS-1, 39.8% of participants disagreed with the statement "frozen vegetables are just as healthy as fresh," and 63% disagreed with the statement "canned fruit is just as healthy as fresh". Some FG participants said canned was "ok" if nothing else was available, while others said they do not buy canned FV because they thought canned FVs had lower quality and less nutritious, especially if they are "cheap" or on sale. The FG participants also perceived fresh as being costly, since fresh FV are perishable and seasonal.

FG participants could name a wide variety of FVs, yet PS-1 participants reported consistently purchasing the same FVs (e.g., apples, bananas, corn, beans and broccoli). Barriers to eating a larger variety of FVs, reported by FG participants, included inexperience in planning and preparing meals, cost and time commitment, seasonality, and coping strategies to get children to eat them. Some responses are included below:

"I make stir fry because my son's not a big vegetable eater but he will eat it (stir fry). Now he likes broccoli, caluliflower, stuff like that."

"I buy fruits and vegetables in season...but I buy broccoli year round."

Theme 3: Grocery shopping and meal preparation habits.

Overall, FG participants presented themselves as savvy shoppers, yet found it difficult to provide nutritious meals for their families even with the additional SNAP benefits. Participants indicated they knew how to eat healthfully, wanted their families to have nutritious food, and reported they read product packaging and nutrition labels. One FG participant stated, "I only buy things for her that are good for her. If she wants sweets we make our own and use less sugar."

When asked about preparing dinner, PS-1 participants (46%) reported they 'rarely' had help. Overall, 80% agreed with the statement, "I would like to serve more balanced meals to my family", while 54% disagreed with the statement "healthy food is time consuming to prepare." Some FG participants acknowledged they have little experience with, or knowledge of planning and preparing healthy meals. For example one participant said, "I think I wish that one of them things I could do is to make a more balanced meals, I try really hard but I don't have all the knowledge that I need to make the balanced meals."

Theme 4: Preferred communication channels.

Half of PS-1 participants indicated that the Internet was the preferred method for finding information on healthful food choices, followed by grocery stores (16%), and magazines (12%). The Internet was the preferred method for participants living in both single adult (51%) and multiple adult households (56%). Those individuals between the ages of 25-34 years reported the Internet as the preferred option (58%) versus those 45-54 years (40%) or 55-64 years (30%). PS-1 participants reported using the following sources for cooking tips and ideas: Internet (~28%) friends and family (~25%), cookbooks (~12%) and television (~12%).

FG-1 and FG-2 participants were asked different questions related to communication channels. When FG-1 participants were asked about US Department of Agriculture (USDA), Food and Nutrition Service materials (e.g., SNAP, The Special Supplemental Nutrition Program for Women, Infants, and Children (WIC), and school materials) as healthful eating resources they reported these resources were not motivating and perceived them as having a "government look". FG-2 participants were asked about Internet usage and on-line social networking opportunities. They reported wanting healthful eating websites to provide actionable information, including food advice, healthful recipes, and preparation shortcuts. Specific suggestions included "fast and easy recipes that kids will eat", "recipes with 5 ingredients or less", "gardening tips (when to plant)", and "age appropriate sections so their children could look for items they could cook." Facebook was the participant's main social media site, more "content" and "more of a network" compared to other sites, and Google their main search site. Finally, the majority of participants stated they like to watch cooking shows (i.e., on the Food Network) to learn about new recipes and preparation tips; however, many felt these recipes were too difficult to replicate. For example, one participant said, "sometimes ingredients are too complicated...".

Theme 5: Responses to previously developed campaigns and messages.

Materials and messages from out-of-state SNAP-Ed funded SM campaigns were tested in FG-1 to determine if they were a "good fit" for our campaign and if the materials could be used "as is" or in a "modified format". Participants were asked to review two campaigns: Iowa's "Pick a Better Snack" and California's "Champions for Change". They preferred Iowa's campaign and indicated that it featured produce items they would buy/eat and responded positively to its message "Ready-to-Serve." For example, one mom expressed, "If you think about a snack versus a candy bar or bag of chips...get some grapes, you don't have to unwrap them, its easy, it's not that hard to eat healthy."

However, Iowa's "Pick a Better Snack" message "It's That Easy" evoked some negative responses indicating it is not always easy to eat healthfully. One comment was, "It's condescending! It's not that easy! Fruits and veggies are expensive, so it's not easy to buy them."

Reponses to the "Champions for Change"; campaign were less favorable. Participants felt that this campaign placed primary responsibility for family food choices on mothers, and that it featured overbearing mothers, with such messages as "My Kitchen, My Rules". They also commented that the kitchen is a family space and not solely mom's space. Participants expected to see media ads for these two campaigns at WIC clinics; however, earlier they indicated that nutrition materials at these clinics did not change their eating behavior. Finally, they reported that both campaigns would have a greater likelihood of changing their FV purchasing behaviors if delivered in a grocery store. Although some FG participants mentioned they would like these messages to come home from school in their children's backpacks, earlier in the FGs they said these materials were treated as junk mail.

After reviewing FG-1 and PS-1 results, it was determined that a new SM campaign should be designed. Components of a new campaign were created including two mock-up campaign names, three logos, and seven messages (, ). FG-2 was used to test these components. Of the name/logo combinations tested, Food Hero name/logo number 1 tested best (). Participants saw themselves as being "Food Heroes" and talked about making their children and others "Food Heroes." They gave feedback on colors and logo use. Participants were asked to rate FV messages () on a scale of 1-10 (1 = very bad; 10 = very good). They preferred positive messages that promoted good role modeling within the family, and did not like negative messages or messages that emphasized their financial struggles. They readily accepted messages that were perceived as genuine, and quickly rejected messages they considered disingenuous regardless if they were true or not. The highest rated message was "canned, frozen, or fresh they all start out the same." The other messages are presented in rank order in .

3.2. Phone Survey 2

The following two key themes/outcomes emerged from PS-2: 1) participant recall and awareness of the Food Hero campaign and 2) beliefs about FVs.

Main Themes

Theme 1: Participant recall and awareness of the Food Hero campaign.

Within the intervention counties, 12% of participants recalled the Food Hero name vs. 3% of participants from the control county. When participants were asked the question, "When you hear the phrase "Food Hero" what is the first word or phrase that you think of?", 60% in the intervention counties correctly interpreted the intended meaning of Food Hero vs. only 23% in the control county. Participants associated Food Hero with eating nutritiously, being a good role model, and eating FVs. For the intervention counties, 68% recalled hearing or seeing at least one of the campaign messages (). The message with the greatest recall (58%) was "Give them more of the good stuff". SNAP households in intervention counties received this message via direct mail, and on billboards in all counties except Josephine County due to a lack of SNAP-Ed approved billboard locations.

Theme 2: Beliefs about fruits and vegetables.

Campaign participants were asked to respond to a series of eight belief statements regarding FV use (). With data combined for all intervention counties, three of these belief statements showed significant change from PS-1 to PS-2 ( p < 0.05). First, participants reported it was easier to get their family to eat fruit. Second, they were less likely to report that it was time consuming to prepare healthful food. Third, they were less likely to report that it was expensive to eat a diet that included "a lot of" FVs.

When comparing PS-2 for the intervention versus the control county for the same eight belief statements () the results were similar except for two belief statements ( p < 0.05). First, the control county had significantly lower confidence in serving balanced meals for their family. Second, they were less likely to report that canned fruit was just as healthy as fresh fruit.

4.1. Study Strengths

Overall, this research has four key strengths. First, to our knowledge this is the first SM campaign, aimed at FV intake for SNAP-eligible families by targeting mothers, that has outlined the formative steps used for campaign development, including implementation and testing of a pilot campaign. Research on formative assessment of FV SM campaigns differed from ours in their breath (national campaigns with broad audiences), focus (local campaigns and audiences), audience (focused on low-income parents not mothers), or provided no assessments (focused on mothers but no assessments reported) [ 15, 51, 52, 53, 54, 55, 56, 57]

Second, only SNAP participants were recruited using a random sample provided by Oregon's DHS. Third, mixed methods were used to gain in-depth understanding of our target mothers. Fourth, rural and urban counties were included to determine if the same campaign worked for these different populations.

4.2. Limitations

Study limitations are primarily related to funder guidelines: (1) The project needed to be completed within 1-year, which limited key development factors (e.g., in-depth message testing, cognitive testing of the belief questions, comprehensive website development, pilot campaign length); and (2) Only direct education and SM were allowed with SNAP-Ed funds, thus, at the time of development we could not fully engage in PSE change strategies. Consequently, we could only pilot test the Food Hero SM campaign for 2 months, with plans for later assessments that would measure change in FV behavior. Finally, the outcomes from the Food Hero SM campaign may not be generalizable to other population groups, since we had a specific target group of low-income mothers with children in the home and who spoke English.

4.3. Implications for Research and Practice

As a result of this research, the Food Hero SM campaign has evolved and is now a statewide campaign. Currently over 125,000 unique users visit FoodHero.org each month, with over 2.2 million page views in 2015. Since the research described in this paper was conducted, ongoing FGs and PSs with the Food Hero primary and secondary audiences have helped to further refine our campaign components. Behavior change assessments are being done to determine changes in FV intake, PSE strategies incorporated, and both English and Spanish language versions of the campaign are available.

Others working to increase FV intake with low-income mothers can use the insights learned from our target population for their own research/programs. Additionally, we provide an example of potential steps to take to create a new campaign and/or test existing campaigns. Finally, perceptions of different forms of FVs gained from this research can be used to frame FV messaging in health promotion.

Oregon SNAP-Ed funding supported the research, but no SNAP-Ed funds were used for publication costs.

The following abbreviations are used in this manuscript:

FV

Fruits and Vegetables

SNAP

Supplemental Nutrition Assistance Program

BRFSS

Behavioral Risk Factor Surveillance System

SNAP-Ed

Supplemental Nutrition Assistance Program-Education

DHS

The Oregon Department of Human Services

PSE

Policy Systems and Environmental change efforts

SCT

Social Cognitive Theory

USDA

United States Department of Agriculture

2. Oregon Department of Human Services July snap caseloads, 2000 through 2013. 2015 Unpublished work.

8. Haynes-Maslow L., Parsons S.E., Wheeler S.B., Leone L.A. A qualitative study of perceived barriers to fruit and vegetable consumption among low-income populations, North Carolina, 2011. Prev. Chronic Dis. 2013 doi: 10.5888/pcd10.120206. [[ PMC free article] [ PubMed] [ CrossRef] Google Scholar]

22. Oregon State University Food Hero Website. [(accessed on 7 September 2016)]. Available online: http://www.foodhero.org/

# data[].attributes.attachment_link.title
Reaching Low-Income Mothers to Improve Family Fruit and Vegetable Intake: Food Hero Social Marketing Campaign-Research Steps, Development and Testing

# data[].attributes.attachment_link.description
The objective of this study was to create/test a social marketing campaign to increase fruit/vegetable (FV) intake within Oregon Supplemental Nutrition Assistance Program (SNAP) eligible families. Focus groups ( n = 2) and pre/post campaign phone surveys ( n = 2082) were conducted in intervention counties (IC) and one control county.

# data[].attributes.headline
Comparison of availability of healthy foods

# data[].attributes.subject
Comparison of availability of healthy foods