Simulated Nutritional and Health Impacts of Restricting Ultra-Processed Food Purchases in the SNAP Program: A NHANES-Based Policy Modeling Study

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Participants in the Supplemental Nutrition Assistance Program (SNAP) consume disproportionately high levels of UPFs, contributing to dietary disparities. Despite this, few policy simulations have quantified the potential health benefits of restricting UPF purchases within SNAP. Objective To estimate the nutritional and cardiometabolic health impacts of restricting UPF purchases in SNAP using nationally representative dietary data and a Monte Carlo policy modeling framework. Methods We conducted a cross-sectional simulation study using NHANES 2007–2020 data from adults aged 20–65. Foods were classified by NOVA criteria, and three scenarios were modeled: isocaloric replacement of 25%, 50%, and 100% of UPFs with minimally processed alternatives. Nutrient shifts (sodium, added sugar, fiber) were estimated for SNAP participants and nonparticipants. Health impacts were simulated by applying meta-analytic effect sizes linking these nutrients to systolic blood pressure (SBP), type 2 diabetes (T2D), and cardiovascular disease (CVD) risk. Results Full UPF replacement (100%) among SNAP participants led to reductions of 257 mg/day sodium, 30.7 g/day added sugar, and a gain of 1.13 g/day fiber. These shifts translated to a 0.64 mmHg SBP reduction, 0.25% relative reduction in T2D risk, and 1.01% relative reduction in CVD risk. Nonparticipants experienced slightly greater improvements. Conclusions Restricting UPF purchases in SNAP could yield meaningful population-level improvements in cardiometabolic health. Though individual risk reductions are modest, large-scale implementation may produce substantial public health benefits and help narrow dietary inequities. Ultra-processed foods SNAP NHANES policy simulation dietary modeling sodium intake added sugar dietary fiber cardiovascular disease type 2 diabetes systolic blood pressure Figures Figure 1 Figure 2 Figure 3 Introduction The Supplemental Nutrition Assistance Program (SNAP) serves as the cornerstone of federal efforts to alleviate food insecurity among low-income Americans, providing monthly benefits to approximately 42 million individuals in 2020 [ 1 ]. Participants in SNAP often confront barriers to accessing healthful foods, resulting in dietary patterns that diverge from national recommendations on nutrient intake and food group consumption [ 1 ]. Systematic reviews demonstrate that, compared with income-eligible nonparticipants, SNAP beneficiaries tend to have lower overall diet quality—characterized by reduced consumption of fruits, vegetables, and whole grains—and higher intake of solid fats, added sugars, and sodium [ 1 ]. Given SNAP’s dual mandate of improving food security and fostering nutritional health, policy interventions that shift purchasing behaviors toward minimally processed foods may yield important public health benefits. Ultra-processed foods (UPFs) have emerged as a major driver of poor diet quality in the United States and globally. Defined by the NOVA classification system, UPFs comprise industrial formulations that incorporate substances rarely used in home kitchens—such as high-fructose corn syrup, hydrogenated oils, and a suite of cosmetic additives—and are engineered for long shelf-life, hyperpalatability, and convenience [ 2 , 3 ]. Unlike unprocessed or minimally processed foods, UPFs undergo multiple sequential industrial processes—fractioning of whole foods, chemical modification of food substances, and inclusion of numerous additives—with the explicit intent of displacing more nutritious alternatives [ 4 ]. In U.S. adults, UPFs now contribute more than 57% of total dietary energy, a trend that has been implicated in the rising prevalence of obesity, metabolic syndrome, type 2 diabetes, cardiovascular disease, and certain cancers [ 5 , 6 ]. Large prospective cohort studies consistently link higher UPF intake to adverse health outcomes. In the French NutriNet-Santé cohort, each 10% increment in the proportion of UPFs was associated with a 12% higher risk of overall cancer and an 11% increased risk of breast cancer [ 7 ]. A 2023 meta-analysis of observational studies further confirmed robust associations between UPF consumption and increased incidence of colorectal, pancreatic, and other site-specific cancers [ 8 ]. Parallel evidence implicates UPFs in cardiometabolic dysfunction: cohort studies reveal that high UPF consumers have 15–53% greater risk of incident type 2 diabetes, and randomized controlled trials demonstrate that ad libitum ultra-processed diets provoke excess energy intake and weight gain even when matched for calories, macronutrients, fiber, sugar, and sodium [ 9 , 10 ]. Such findings underscore the multifaceted biological and behavioral pathways—hyperpalatability, disruption of satiety signaling, and fructose-driven de novo lipogenesis—through which UPFs heighten disease risk. Despite compelling evidence linking UPFs to poor health, few studies have modeled the potential impact of policy levers to curb UPF consumption, particularly within SNAP. Economic analyses suggest that fiscal incentives and purchase restrictions—such as banning sugar-sweetened beverages or incentivizing fruit and vegetable purchases—can improve diet quality, yet the magnitude of nutritional and clinical benefits remains uncertain [ 1 ]. Simulation studies using nationally representative data, such as the National Health and Nutrition Examination Survey (NHANES), allow policymakers to estimate how hypothetical interventions might alter nutrient intakes and downstream health outcomes. Monte Carlo methods, incorporating survey weights and within-person variability, facilitate robust usual-intake modeling and probabilistic health impact assessment, translating nutrient shifts into metrics like systolic blood pressure (SBP) reduction and relative risk changes for type 2 diabetes (T2D) and cardiovascular disease (CVD) [ 11 , 12 ]. Here, we conduct a cross-sectional policy simulation leveraging NHANES 2007–2020 dietary data to estimate the nutritional and cardiometabolic health implications of restricting UPF purchases among SNAP participants. We model graded replacement scenarios—25%, 50%, and 100% isocaloric substitution of UPFs with minimally processed options—and apply meta-analytic effect sizes for sodium, added sugar, and fiber to project changes in SBP, T2D risk, and CVD risk. Our objectives are to quantify potential nutrient improvements attainable through UPF restrictions in SNAP and to contextualize these results within the broader literature on dietary interventions, offering evidence to inform future SNAP policy reforms. Methods Study Design and Data Source We conducted a cross-sectional policy simulation study using data from the National Health and Nutrition Examination Survey (NHANES), pooling five survey cycles from 2007–2008 through 2017–2020. The analytic sample included U.S. adults aged 18 years and older with valid Day 1 24-hour dietary recall data and complete demographic and dietary information. Individuals were categorized by Supplemental Nutrition Assistance Program (SNAP) participation status, based on self-report in income and food security questionnaires. Dietary Assessment and UPF Classification Dietary intake was extracted from the Day 1 individual food file and total nutrient intake file. Each food item was linked to its USDA food code (DR1IFDCD) and classified using a logic-based implementation of the NOVA system. Ultra-processed foods (UPFs) were defined as NOVA Group 4 items and identified through structured keyword matching with USDA FNDDS 2017–2018 food descriptions. Simulation Scenarios and Substitution Logic Three restriction scenarios were modeled: Scenario A (25% replacement of UPFs), Scenario B (50% replacement), and Scenario C (100% replacement). Replacements were isocaloric and assumed to reflect nutrient profiles of minimally processed foods, modeled as containing 50% less sodium, 80% less added sugar, and 50% more fiber per unit energy than the UPFs removed. Total daily intakes of sodium, added sugar, fiber, and energy were recalculated for each participant under each scenario. Statistical Analysis and Usual Intake Modeling Survey weights, primary sampling units, and strata were incorporated using the survey and srvyr packages in R. Nutrient outcomes were summarized using survey-weighted means and 95% confidence intervals. Usual intake was estimated through Monte Carlo simulation (10,000 draws per participant) incorporating a 20% within-person variation factor. To estimate health impacts, we applied effect sizes from published meta-analyses linking dietary factors to cardiometabolic outcomes. We modeled a 2.5 mmHg systolic blood pressure (SBP) reduction per 1,000 mg sodium reduction, a 0.8% reduction in type 2 diabetes (T2D) risk per 10 g reduction in added sugar, and a 0.9% reduction in cardiovascular disease (CVD) risk per 10 g increase in fiber. Monte Carlo uncertainty was applied to the nutrient and outcome effects. Results Baseline Characteristics of the Study Population by SNAP Status At baseline, SNAP participants (n = 767) were more likely to be female (56.0%) and identify as Non-Hispanic Black (32.1%) or Mexican American (15.1%), whereas non-participants (n = 767) were more likely to be Non-Hispanic White (59.3%) as represented in Table 1. The average total energy intake was 1884.5 kcal/day (SD = 542.3) for SNAP participants compared to 1950.6 kcal/day (SD = 531.9) in non-participants. Mean sodium intake was 3452.1 mg/day (SD = 1104.2) in the SNAP group and 3610.2 mg/day (SD = 1010.8) among non-participants. Added sugar intake averaged 127.4 g/day (SD = 45.2) for SNAP participants and 133.1 g/day (SD = 41.8) for non-participants, while fiber intake was slightly lower among SNAP participants (15.1 g/day, SD = 5.6) versus non-participants (16.2 g/day, SD = 5.4). The proportion of energy derived from ultra-processed foods was higher in the SNAP group (58.2% ± 11.5%) compared to non-participants (55.6% ± 10.7%). Figure 1 represents the usual intake distributions of sodium, sugar, and fiber estimated via Monte Carlo simulation. Variable SNAP Participant (n = 767) Non-Participant (n = 5563) Gender Male (%) 44.0 47.3 Female (%) 56.0 52.7 Race/Ethnicity Mexican American (%) 15.1 6.7 Other Hispanic (%) 9.8 8.1 Non-Hispanic White (%) 36.7 59.3 Non-Hispanic Black (%) 32.1 23.7 Other Race (%) 6.3 2.2 Continuous Variables Total kcal/day 1884.5 ± 542.3 1950.6 ± 531.9 Sodium (mg/day) 3452.1 ± 1104.2 3610.2 ± 1010.8 Sugar (g/day) 127.4 ± 45.2 133.1 ± 41.8 Fiber (g/day) 15.1 ± 5.6 16.2 ± 5.4 % Energy from UPFs 58.2 ± 11.5 55.6 ± 10.7 Values are presented as mean ± SD for continuous variables, and as column percentages for categorical variables. Simulated Nutrient Changes Simulation of UPF restriction scenarios showed progressive nutrient improvements. Under Scenario A (25% replacement), SNAP participants had reductions of 64.2 mg/day in sodium, 7.7 g/day in sugar, and a gain of 0.28 g/day in fiber. Nonparticipants experienced reductions of 73.5 mg/day in sodium, 7.9 g/day in sugar, and an increase of 0.31 g/day in fiber. In Scenario B (50%), sodium intake declined by 128.4 mg/day and 147.1 mg/day for SNAP and non-SNAP groups, respectively, while sugar dropped by approximately 15.4 g/day and fiber increased by about 0.56 to 0.62 g/day. Scenario C (100%) yielded the largest changes, with sodium reductions of 256.7 mg/day in SNAP participants and 294.1 mg/day in nonparticipants; sugar dropped by 30.7 g/day and 31.4 g/day respectively, while fiber increased by 1.13 g/day and 1.25 g/day (Table 2 , Fig. 2 ). Table 2 Simulated Changes in Nutrient Intake by Scenario and SNAP Status Scenario SNAP Status Δ Sodium (mg) Δ Sugar (g) Δ Fiber (g) 25% SNAP Participant -64.2 -7.68 + 0.28 Non-Participant -73.5 -7.86 + 0.31 50% SNAP Participant -128.4 -15.37 + 0.56 Non-Participant -147.1 -15.72 + 0.62 100% SNAP Participant -256.7 -30.74 + 1.13 Non-Participant -294.1 -31.44 + 1.25 Health Impact Simulation Monte Carlo simulation of health outcomes (Fig. 3 ) indicated that full UPF replacement (Scenario C) would reduce SBP by 0.64 mmHg (95% CI: 0.51, 0.77) in SNAP participants and 0.74 mmHg (95% CI: 0.59, 0.88) in nonparticipants. Corresponding reductions in T2D risk were 0.25% (95% CI: 0.20%, 0.29%) and 0.25% (95% CI: 0.20%, 0.30%), and CVD risk reductions were 1.01% (95% CI: 0.81%, 1.21%) and 1.12% (95% CI: 0.90%, 1.34%) respectively. Scenario B (50% replacement) produced intermediate health benefits with 0.32–0.37 mmHg reductions in SBP and approximately 0.12% reductions in diabetes risk. Discussion The simulated restriction of ultra-processed foods (UPFs) in the Supplemental Nutrition Assistance Program (SNAP) produced modest per-person nutrient shifts—daily reductions of ~ 257 mg sodium, ~ 30.7 g added sugar, and increases of ~ 1.13 g fiber—yet even small improvements can yield meaningful population-level health dividends when scaled to the 42 million SNAP beneficiaries. Our Monte Carlo usual-intake modeling approach, adapted from prior NHANES-based policy simulations [ 13 ], aligns with established methods for projecting dietary interventions into cardiometabolic risk changes. At the individual level, − 256.7 mg sodium corresponds to a 0.64 mm Hg drop in systolic blood pressure (SBP), and ~ 30 g sugar lowering translates to a 0.25% relative reduction in type 2 diabetes (T2D) risk—effects consistent with dose–response meta-analyses showing 2.5 mm Hg SBP reduction per 1 000 mg sodium and 0.8% T2D risk reduction per 10 g sugar. Although sub-1 mm Hg SBP effects appear small in isolation, public health models estimate that even 1 mm Hg population-wide SBP reduction can prevent thousands of strokes and myocardial infarctions annually. Our findings build on a robust body of evidence linking UPF consumption—now exceeding 57% of U.S. caloric intake—to poor health outcomes. Systematic reviews and umbrella analyses document associations between high UPF intake and increased risks of all-cause mortality, obesity, hypertension, T2D, cardiovascular disease (CVD), cancer, and mental health disorders [ 14 – 16 ]. Experimental trials show that ultra-processed diets provoke excess energy intake and weight gain even when matched for calories and macronutrients with unprocessed diets, while cohort data from NutriNet-Santé and other longitudinal studies report 10% UPF energy increments associated with 12% higher overall cancer risk and 53% greater T2D incidence. Mechanistic pathways include dysregulated appetite signaling from hyperpalatable additives, fructose-driven hepatic lipogenesis, inflammatory responses to food additives, and gut–brain axis perturbations; moreover, emerging data link UPF intake to common mental disorders (OR 1.53) and neurodevelopmental risks in pregnancy [ 14 , 17 ]. Despite this compelling evidence, few studies have estimated the impact of specific SNAP policy levers on UPF consumption and downstream health. Fiscal measures—such as national UPF taxes—are projected to reduce purchases and improve diet quality in modeling studies [ 18 ], while mandatory front-of-pack labeling (FOPL) yields significant sodium and sugar declines in simulation [ 19 ]. SNAP-specific microsimulations suggest that removing sugar-sweetened beverages (SSBs) could reduce obesity, CVD, and T2D burden [ 20 ], yet implementation risks, beneficiary acceptability, and potential compensation through non-SNAP funds complicate policy translation. Indeed, qualitative analyses reveal that SNAP restrictions may stigmatize participants without improving long-term diet quality, whereas incentive programs (e.g., fruit and vegetable vouchers) demonstrate more consistent benefits [ 21 ]. Our study fills a critical gap by quantifying the scale of potential nutrient and risk shifts under graded UPF restriction scenarios, providing evidence to weigh restriction versus incentive strategies in future SNAP reforms. Key limitations might temper the interpretation of our results. First, UPF classification relied on NOVA coding of 2017–2018 FNDDS descriptions; though validated approaches yield 80–90% accuracy [ 22 , 23 ], cycle-specific crosswalks and manual audits could reduce misclassification. The NOVA framework, while widely used, faces critiques over ambiguous definitions and potential for inconsistent assignments [ 24 , 25 ]. Second, the substitution logic—50% sodium cut, 80% sugar cut, and 50% fiber boost—reflects optimistic nutrient profiles of minimally processed alternatives; real-world compliance may be lower, and cross-price elasticities could lead households to offset SNAP-restricted UPFs with purchases outside the program [ 26 , 27 ]. Third, our Monte Carlo simulations capture within-person variability but do not propagate complex-survey design uncertainty introduced by nutrient recalculations; future analyses should incorporate replicate weights or Taylor linearization to refine confidence intervals. Cost-effectiveness and policy feasibility remain open questions. Early economic models demonstrate that modest dietary interventions can yield favorable cost-utility ratios when accounting for healthcare savings from reduced CVD and T2D burden [ 28 , 29 ], yet full economic evaluations of SNAP UPF restrictions—including administrative costs, lost retail sales, and potential welfare impacts—are lacking. Pilot trials or natural experiments (e.g., state-level SNAP waivers with nutrition incentives) could provide real-world data to complement our simulation. Finally, long-term monitoring of dietary patterns, clinical biomarkers, and program participation rates will be critical to ensure that SNAP reforms achieve intended health goals without unintended adverse effects. In conclusion, while complete UPF replacement within SNAP yields modest per-person nutrient and risk reductions, the aggregate public health impact could be substantial when scaled to millions of beneficiaries. Our NHANES-based simulation offers a rigorous, transparent framework for policymakers to estimate the nutritional consequences of restricting UPF purchases. Future work should refine classification methods, incorporate behavioral economics, expand equity-focused subgroup analyses, and evaluate cost-effectiveness to inform balanced SNAP policy reforms that advance both food security and nutritional health. Declarations Ethics approval and consent to participate This study used de‑identified data from the publicly available NHANES database and did not involve direct patient contact or the use of individually identifiable health information. Under the U.S. Common Rule, research using only publicly available, de‑identified data is exempt from institutional review board oversight; therefore, ethics approval and patient consent were not required. Consent for publication Not applicable. Availability of data and materials The dataset analyzed during the current study is available in the NHANES repository: https://www.cdc.gov/nchs/nhanes/. Competing interests The authors declare that they have no competing interests. Funding No external funding was received for this work. Authors’ contributions AH conceived the study, performed data extraction and statistical analyses, and drafted the manuscript. PS assisted with critical revision of the manuscript. All authors read and approved the final manuscript. References Andreyeva T, Tripp AS, Schwartz MB (2015) Dietary Quality of Americans by Supplemental Nutrition Assistance Program Participation Status: A Systematic Review. Am J Prev Med 49(4):594–604 Monteiro CA, Cannon G, Levy RB, Moubarac JC, Louzada ML, Rauber F, Khandpur N, Cediel G, Neri D, Martinez-Steele E et al (2019) Ultra-processed foods: what they are and how to identify them. Public Health Nutr 22(5):936–941 Monteiro CA, Cannon G, Moubarac JC, Levy RB, Louzada MLC, Jaime PC (2018) The UN Decade of Nutrition, the NOVA food classification and the trouble with ultra-processing. Public Health Nutr 21(1):5–17 Monteiro CA, Levy RB, Claro RM, Castro IR, Cannon G (2010) A new classification of foods based on the extent and purpose of their processing. Cad Saude Publica 26(11):2039–2049 Monteiro CA, Astrup A (2022) Does the concept of ultra-processed foods help inform dietary guidelines, beyond conventional classification systems? YES. Am J Clin Nutr 116(6):1476–1481 Gibney MJ (2019) Ultra-Processed Foods: Definitions and Policy Issues. Curr Dev Nutr 3(2):nzy077 Fiolet T, Srour B, Sellem L, Kesse-Guyot E, Allès B, Méjean C, Deschasaux M, Fassier P, Latino-Martel P, Beslay M et al (2018) Consumption of ultra-processed foods and cancer risk: results from NutriNet-Santé prospective cohort. BMJ 360:k322 Isaksen IM, Dankel SN (2023) Ultra-processed food consumption and cancer risk: A systematic review and meta-analysis. Clin Nutr 42(6):919–928 Srour B, Fezeu LK, Kesse-Guyot E, Allès B, Debras C, Druesne-Pecollo N, Chazelas E, Deschasaux M, Hercberg S, Galan P et al (2020) Ultraprocessed Food Consumption and Risk of Type 2 Diabetes Among Participants of the NutriNet-Santé Prospective Cohort. JAMA Intern Med 180(2):283–291 Hall KD, Ayuketah A, Brychta R, Cai H, Cassimatis T, Chen KY, Chung ST, Costa E, Courville A, Darcey V et al (2019) Ultra-Processed Diets Cause Excess Calorie Intake and Weight Gain: An Inpatient Randomized Controlled Trial of Ad Libitum Food Intake. Cell Metab 30(1):67–77e63 He FJ, MacGregor GA (2011) Salt reduction lowers cardiovascular risk: meta-analysis of outcome trials. Lancet 378(9789):380–382 He FJ, MacGregor GA Effect of longer-term modest salt reduction on blood pressure. Cochrane Database Syst Rev 2004(3):Cd004937 Grummon AH, Smith NR, Golden SD, Frerichs L, Taillie LS, Brewer NT (2019) Health Warnings on Sugar-Sweetened Beverages: Simulation of Impacts on Diet and Obesity Among U.S. Adults. Am J Prev Med 57(6):765–774 Lane MM, Gamage E, Du S, Ashtree DN, McGuinness AJ, Gauci S, Baker P, Lawrence M, Rebholz CM, Srour B et al (2024) Ultra-processed food exposure and adverse health outcomes: umbrella review of epidemiological meta-analyses. BMJ 384:e077310 Pagliai G, Dinu M, Madarena MP, Bonaccio M, Iacoviello L, Sofi F (2021) Consumption of ultra-processed foods and health status: a systematic review and meta-analysis. Br J Nutr 125(3):308–318 Wang Z, Lu C, Cui L, Fenfen E, Shang W, Wang Z, Song G, Yang K, Li X (2024) Consumption of ultra-processed foods and multiple health outcomes: An umbrella study of meta-analyses. Food Chem 434:137460 Talebi S, Mehrabani S, Ghoreishy SM, Wong A, Moghaddam A, Feyli PR, Amirian P, Zarpoosh M, Kermani MAH, Moradi S (2024) The association between ultra-processed food and common pregnancy adverse outcomes: a dose-response systematic review and meta-analysis. BMC Pregnancy Childbirth 24(1):369 Valizadeh P, Ng SW (2024) Promoting Healthier Purchases: Ultraprocessed Food Taxes and Minimally Processed Foods Subsidies for the Low Income. Am J Prev Med 67(1):3–14 Flexner N, Zaltz D, Greenthal E, Musicus AA, Ahmed M, L’Abbe MR (2025) Estimating the dietary and health impact of implementing mandatory front-of-package nutrient disclosures in the US: A policy scenario modeling analysis. PLoS ONE 20(2):e0312638 Leung CW, Wolfson JA (2025) A Path Forward for Strengthening SNAP Nutrition Policy—Making the US Healthy Again. JAMA Health Forum 6(5):e251115–e251115 Parks CA, Han P, Fricke HE, Parker HA, Hesterman OB, Yaroch AL (2021) Reducing food insecurity and improving fruit and vegetable intake through a nutrition incentive program in Michigan, USA. SSM - Popul Health 15:100898 Sneed NM, Ukwuani S, Sommer EC, Samuels LR, Truesdale KP, Matheson D, Noerper TE, Barkin SL, Heerman WJ (2023) Reliability and validity of assigning ultraprocessed food categories to 24-h dietary recall data. Am J Clin Nutr 117(1):182–190 Steele EM, O'Connor LE, Juul F, Khandpur N, Galastri Baraldi L, Monteiro CA, Parekh N, Herrick KA (2023) Identifying and Estimating Ultraprocessed Food Intake in the US NHANES According to the Nova Classification System of Food Processing. J Nutr 153(1):225–241 Braesco V, Souchon I, Sauvant P, Haurogné T, Maillot M, Féart C, Darmon N (2022) Ultra-processed foods: how functional is the NOVA system? Eur J Clin Nutr 76(9):1245–1253 Jung S, Kim JY, Park S, Lee JE (2025) Potential misclassification of ultra-processed foods across studies and the need for a unified classification system: a scoping review. Nutr Res Pract 19(3):331–344 Leung CW, Fulay AP, Parnarouskis L, Martinez-Steele E, Gearhardt AN, Wolfson JA (2022) Food insecurity and ultra-processed food consumption: the modifying role of participation in the Supplemental Nutrition Assistance Program (SNAP). Am J Clin Nutr 116(1):197–205 Fulay AP, Baylin A, Wolfson JA, Lee JM, Martinez-Steele E, Leung CW (2025) Associations between Food Insecurity and Supplemental Nutrition Assistance Program (SNAP) participation with ultra-processed food intake in lower-income U.S. adolescents. J Nutritional Sci 14:e41 Dötsch-Klerk M, Bruins MJ, Detzel P, Martikainen J, Nergiz-Unal R, Roodenburg AJC, Pekcan AG (2023) Modelling health and economic impact of nutrition interventions: a systematic review. Eur J Clin Nutr 77(4):413–426 Emmert-Fees KMF, Karl FM, von Philipsborn P, Rehfuess EA, Laxy M (2021) Simulation Modeling for the Economic Evaluation of Population-Based Dietary Policies: A Systematic Scoping Review. Adv Nutr 12(5):1957–1995 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6897045","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":471426899,"identity":"dbd3565a-2a2c-46e9-995d-b47c7f826baf","order_by":0,"name":"Ali Hemade","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYDACdsY2EMXYwN98AEhLyBDWwgzTInEsAaSFhwgtDGwQLQw5BiAGYS38zcxtDz7m2Mn2M5z5/OpGjQUPA/vhoxvwaZE4zNhuOHNbsvHM5t5t1jnHgA7jSUu7gdeaw4xt0rzbmBM3HDi7zTiHDahFgscMrxZ5kJa/2+oT9x/IeWac848ILQYgLYzbDiduYMhhfpzbRoQWQ6AWyd5tx41n3DhmxpzbJ8HDRsgvcsfbn0n83FYt29/f/Phzzrc6OX72w8fwex8JsEmASWKVgwDzB1JUj4JRMApGwcgBACr6SekvNCM5AAAAAElFTkSuQmCC","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Ali","middleName":"","lastName":"Hemade","suffix":""},{"id":471426900,"identity":"28866baa-0676-4f1b-8f11-96ab0d636679","order_by":1,"name":"Pascale Salameh","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Pascale","middleName":"","lastName":"Salameh","suffix":""}],"badges":[],"createdAt":"2025-06-15 07:45:38","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6897045/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6897045/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84921151,"identity":"26db3ab0-92f4-4e66-9aba-ef4d0b96be0c","added_by":"auto","created_at":"2025-06-18 19:44:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":74094,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of Baseline Usual Nutrient Intake by SNAP Participation. Usual intake distributions of sodium, sugar, and fiber estimated via Monte Carlo simulation (n = 10,000 draws per individual).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6897045/v1/4fbaa07016a34dbe3e1e693d.png"},{"id":84920168,"identity":"ebeb007d-c11f-454c-a8a3-ef535425ac35","added_by":"auto","created_at":"2025-06-18 19:36:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":77573,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in Daily Nutrient Intake Across UPF Restriction Scenarios. Nutrient shifts were proportional to restriction intensity across SNAP and non-SNAP groups.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6897045/v1/a86056e5458cfc96f372495a.png"},{"id":84920164,"identity":"4d677b83-f521-4c55-b745-639005e46d6e","added_by":"auto","created_at":"2025-06-18 19:36:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":73640,"visible":true,"origin":"","legend":"\u003cp\u003eSimulated Health Impacts of UPF Restriction. Probabilistic projections of changes in SBP, diabetes risk, and CVD risk from Scenarios A–C based on nutrient changes and meta-analytic effect sizes.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6897045/v1/27838322d340ab8cfd6a2795.png"},{"id":84921156,"identity":"9a194911-9164-4a9a-b352-2cb223f08c78","added_by":"auto","created_at":"2025-06-18 19:44:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":814949,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6897045/v1/a59391e1-0146-46ae-adf4-121d937647fa.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eSimulated Nutritional and Health Impacts of Restricting Ultra-Processed Food Purchases in the SNAP Program: A NHANES-Based Policy Modeling Study\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe Supplemental Nutrition Assistance Program (SNAP) serves as the cornerstone of federal efforts to alleviate food insecurity among low-income Americans, providing monthly benefits to approximately 42\u0026nbsp;million individuals in 2020 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Participants in SNAP often confront barriers to accessing healthful foods, resulting in dietary patterns that diverge from national recommendations on nutrient intake and food group consumption [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Systematic reviews demonstrate that, compared with income-eligible nonparticipants, SNAP beneficiaries tend to have lower overall diet quality\u0026mdash;characterized by reduced consumption of fruits, vegetables, and whole grains\u0026mdash;and higher intake of solid fats, added sugars, and sodium [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Given SNAP\u0026rsquo;s dual mandate of improving food security and fostering nutritional health, policy interventions that shift purchasing behaviors toward minimally processed foods may yield important public health benefits.\u003c/p\u003e \u003cp\u003eUltra-processed foods (UPFs) have emerged as a major driver of poor diet quality in the United States and globally. Defined by the NOVA classification system, UPFs comprise industrial formulations that incorporate substances rarely used in home kitchens\u0026mdash;such as high-fructose corn syrup, hydrogenated oils, and a suite of cosmetic additives\u0026mdash;and are engineered for long shelf-life, hyperpalatability, and convenience [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Unlike unprocessed or minimally processed foods, UPFs undergo multiple sequential industrial processes\u0026mdash;fractioning of whole foods, chemical modification of food substances, and inclusion of numerous additives\u0026mdash;with the explicit intent of displacing more nutritious alternatives [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In U.S. adults, UPFs now contribute more than 57% of total dietary energy, a trend that has been implicated in the rising prevalence of obesity, metabolic syndrome, type 2 diabetes, cardiovascular disease, and certain cancers [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLarge prospective cohort studies consistently link higher UPF intake to adverse health outcomes. In the French NutriNet-Sant\u0026eacute; cohort, each 10% increment in the proportion of UPFs was associated with a 12% higher risk of overall cancer and an 11% increased risk of breast cancer [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. A 2023 meta-analysis of observational studies further confirmed robust associations between UPF consumption and increased incidence of colorectal, pancreatic, and other site-specific cancers [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Parallel evidence implicates UPFs in cardiometabolic dysfunction: cohort studies reveal that high UPF consumers have 15\u0026ndash;53% greater risk of incident type 2 diabetes, and randomized controlled trials demonstrate that ad libitum ultra-processed diets provoke excess energy intake and weight gain even when matched for calories, macronutrients, fiber, sugar, and sodium [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Such findings underscore the multifaceted biological and behavioral pathways\u0026mdash;hyperpalatability, disruption of satiety signaling, and fructose-driven de novo lipogenesis\u0026mdash;through which UPFs heighten disease risk.\u003c/p\u003e \u003cp\u003eDespite compelling evidence linking UPFs to poor health, few studies have modeled the potential impact of policy levers to curb UPF consumption, particularly within SNAP. Economic analyses suggest that fiscal incentives and purchase restrictions\u0026mdash;such as banning sugar-sweetened beverages or incentivizing fruit and vegetable purchases\u0026mdash;can improve diet quality, yet the magnitude of nutritional and clinical benefits remains uncertain [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Simulation studies using nationally representative data, such as the National Health and Nutrition Examination Survey (NHANES), allow policymakers to estimate how hypothetical interventions might alter nutrient intakes and downstream health outcomes. Monte Carlo methods, incorporating survey weights and within-person variability, facilitate robust usual-intake modeling and probabilistic health impact assessment, translating nutrient shifts into metrics like systolic blood pressure (SBP) reduction and relative risk changes for type 2 diabetes (T2D) and cardiovascular disease (CVD) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHere, we conduct a cross-sectional policy simulation leveraging NHANES 2007\u0026ndash;2020 dietary data to estimate the nutritional and cardiometabolic health implications of restricting UPF purchases among SNAP participants. We model graded replacement scenarios\u0026mdash;25%, 50%, and 100% isocaloric substitution of UPFs with minimally processed options\u0026mdash;and apply meta-analytic effect sizes for sodium, added sugar, and fiber to project changes in SBP, T2D risk, and CVD risk. Our objectives are to quantify potential nutrient improvements attainable through UPF restrictions in SNAP and to contextualize these results within the broader literature on dietary interventions, offering evidence to inform future SNAP policy reforms.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e \u003cb\u003eStudy Design and Data Source\u003c/b\u003e We conducted a cross-sectional policy simulation study using data from the National Health and Nutrition Examination Survey (NHANES), pooling five survey cycles from 2007\u0026ndash;2008 through 2017\u0026ndash;2020. The analytic sample included U.S. adults aged 18 years and older with valid Day 1 24-hour dietary recall data and complete demographic and dietary information. Individuals were categorized by Supplemental Nutrition Assistance Program (SNAP) participation status, based on self-report in income and food security questionnaires.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDietary Assessment and UPF Classification\u003c/b\u003e Dietary intake was extracted from the Day 1 individual food file and total nutrient intake file. Each food item was linked to its USDA food code (DR1IFDCD) and classified using a logic-based implementation of the NOVA system. Ultra-processed foods (UPFs) were defined as NOVA Group 4 items and identified through structured keyword matching with USDA FNDDS 2017\u0026ndash;2018 food descriptions.\u003c/p\u003e \u003cp\u003e\u003cb\u003eSimulation Scenarios and Substitution Logic\u003c/b\u003e Three restriction scenarios were modeled: Scenario A (25% replacement of UPFs), Scenario B (50% replacement), and Scenario C (100% replacement). Replacements were isocaloric and assumed to reflect nutrient profiles of minimally processed foods, modeled as containing 50% less sodium, 80% less added sugar, and 50% more fiber per unit energy than the UPFs removed. Total daily intakes of sodium, added sugar, fiber, and energy were recalculated for each participant under each scenario.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStatistical Analysis and Usual Intake Modeling\u003c/b\u003e Survey weights, primary sampling units, and strata were incorporated using the survey and srvyr packages in R. Nutrient outcomes were summarized using survey-weighted means and 95% confidence intervals. Usual intake was estimated through Monte Carlo simulation (10,000 draws per participant) incorporating a 20% within-person variation factor. To estimate health impacts, we applied effect sizes from published meta-analyses linking dietary factors to cardiometabolic outcomes. We modeled a 2.5 mmHg systolic blood pressure (SBP) reduction per 1,000 mg sodium reduction, a 0.8% reduction in type 2 diabetes (T2D) risk per 10 g reduction in added sugar, and a 0.9% reduction in cardiovascular disease (CVD) risk per 10 g increase in fiber. Monte Carlo uncertainty was applied to the nutrient and outcome effects.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eBaseline Characteristics of the Study Population by SNAP Status\u003c/h2\u003e \u003cp\u003eAt baseline, SNAP participants (n\u0026thinsp;=\u0026thinsp;767) were more likely to be female (56.0%) and identify as Non-Hispanic Black (32.1%) or Mexican American (15.1%), whereas non-participants (n\u0026thinsp;=\u0026thinsp;767) were more likely to be Non-Hispanic White (59.3%) as represented in Table\u0026nbsp;1. The average total energy intake was 1884.5 kcal/day (SD\u0026thinsp;=\u0026thinsp;542.3) for SNAP participants compared to 1950.6 kcal/day (SD\u0026thinsp;=\u0026thinsp;531.9) in non-participants. Mean sodium intake was 3452.1 mg/day (SD\u0026thinsp;=\u0026thinsp;1104.2) in the SNAP group and 3610.2 mg/day (SD\u0026thinsp;=\u0026thinsp;1010.8) among non-participants. Added sugar intake averaged 127.4 g/day (SD\u0026thinsp;=\u0026thinsp;45.2) for SNAP participants and 133.1 g/day (SD\u0026thinsp;=\u0026thinsp;41.8) for non-participants, while fiber intake was slightly lower among SNAP participants (15.1 g/day, SD\u0026thinsp;=\u0026thinsp;5.6) versus non-participants (16.2 g/day, SD\u0026thinsp;=\u0026thinsp;5.4). The proportion of energy derived from ultra-processed foods was higher in the SNAP group (58.2% \u0026plusmn; 11.5%) compared to non-participants (55.6% \u0026plusmn; 10.7%). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e represents the usual intake distributions of sodium, sugar, and fiber estimated via Monte Carlo simulation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSNAP Participant (n\u0026thinsp;=\u0026thinsp;767)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-Participant (n\u0026thinsp;=\u0026thinsp;5563)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace/Ethnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Hispanic (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Race (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContinuous Variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal kcal/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1884.5\u0026thinsp;\u0026plusmn;\u0026thinsp;542.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1950.6\u0026thinsp;\u0026plusmn;\u0026thinsp;531.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSodium (mg/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3452.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1104.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3610.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1010.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSugar (g/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e127.4\u0026thinsp;\u0026plusmn;\u0026thinsp;45.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e133.1\u0026thinsp;\u0026plusmn;\u0026thinsp;41.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFiber (g/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% Energy from UPFs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58.2\u0026thinsp;\u0026plusmn;\u0026thinsp;11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55.6\u0026thinsp;\u0026plusmn;\u0026thinsp;10.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eValues are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD for continuous variables, and as column percentages for categorical variables.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSimulated Nutrient Changes\u003c/b\u003e Simulation of UPF restriction scenarios showed progressive nutrient improvements. Under Scenario A (25% replacement), SNAP participants had reductions of 64.2 mg/day in sodium, 7.7 g/day in sugar, and a gain of 0.28 g/day in fiber. Nonparticipants experienced reductions of 73.5 mg/day in sodium, 7.9 g/day in sugar, and an increase of 0.31 g/day in fiber. In Scenario B (50%), sodium intake declined by 128.4 mg/day and 147.1 mg/day for SNAP and non-SNAP groups, respectively, while sugar dropped by approximately 15.4 g/day and fiber increased by about 0.56 to 0.62 g/day. Scenario C (100%) yielded the largest changes, with sodium reductions of 256.7 mg/day in SNAP participants and 294.1 mg/day in nonparticipants; sugar dropped by 30.7 g/day and 31.4 g/day respectively, while fiber increased by 1.13 g/day and 1.25 g/day (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSimulated Changes in Nutrient Intake by Scenario and SNAP Status\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScenario\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSNAP Status\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eΔ Sodium (mg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eΔ Sugar (g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eΔ Fiber (g)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSNAP Participant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-64.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-7.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-Participant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-73.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-7.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSNAP Participant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-128.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-15.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-Participant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-147.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-15.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSNAP Participant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-256.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-30.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;1.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-Participant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-294.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-31.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;1.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eHealth Impact Simulation\u003c/b\u003e Monte Carlo simulation of health outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) indicated that full UPF replacement (Scenario C) would reduce SBP by 0.64 mmHg (95% CI: 0.51, 0.77) in SNAP participants and 0.74 mmHg (95% CI: 0.59, 0.88) in nonparticipants. Corresponding reductions in T2D risk were 0.25% (95% CI: 0.20%, 0.29%) and 0.25% (95% CI: 0.20%, 0.30%), and CVD risk reductions were 1.01% (95% CI: 0.81%, 1.21%) and 1.12% (95% CI: 0.90%, 1.34%) respectively. Scenario B (50% replacement) produced intermediate health benefits with 0.32\u0026ndash;0.37 mmHg reductions in SBP and approximately 0.12% reductions in diabetes risk.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe simulated restriction of ultra-processed foods (UPFs) in the Supplemental Nutrition Assistance Program (SNAP) produced modest per-person nutrient shifts\u0026mdash;daily reductions of ~\u0026thinsp;257 mg sodium, ~\u0026thinsp;30.7 g added sugar, and increases of ~\u0026thinsp;1.13 g fiber\u0026mdash;yet even small improvements can yield meaningful population-level health dividends when scaled to the 42\u0026nbsp;million SNAP beneficiaries. Our Monte Carlo usual-intake modeling approach, adapted from prior NHANES-based policy simulations [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], aligns with established methods for projecting dietary interventions into cardiometabolic risk changes. At the individual level, \u0026minus;\u0026thinsp;256.7 mg sodium corresponds to a 0.64 mm Hg drop in systolic blood pressure (SBP), and ~\u0026thinsp;30 g sugar lowering translates to a 0.25% relative reduction in type 2 diabetes (T2D) risk\u0026mdash;effects consistent with dose\u0026ndash;response meta-analyses showing 2.5 mm Hg SBP reduction per 1 000 mg sodium and 0.8% T2D risk reduction per 10 g sugar. Although sub-1 mm Hg SBP effects appear small in isolation, public health models estimate that even 1 mm Hg population-wide SBP reduction can prevent thousands of strokes and myocardial infarctions annually.\u003c/p\u003e \u003cp\u003eOur findings build on a robust body of evidence linking UPF consumption\u0026mdash;now exceeding 57% of U.S. caloric intake\u0026mdash;to poor health outcomes. Systematic reviews and umbrella analyses document associations between high UPF intake and increased risks of all-cause mortality, obesity, hypertension, T2D, cardiovascular disease (CVD), cancer, and mental health disorders [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Experimental trials show that ultra-processed diets provoke excess energy intake and weight gain even when matched for calories and macronutrients with unprocessed diets, while cohort data from NutriNet-Sant\u0026eacute; and other longitudinal studies report 10% UPF energy increments associated with 12% higher overall cancer risk and 53% greater T2D incidence. Mechanistic pathways include dysregulated appetite signaling from hyperpalatable additives, fructose-driven hepatic lipogenesis, inflammatory responses to food additives, and gut\u0026ndash;brain axis perturbations; moreover, emerging data link UPF intake to common mental disorders (OR 1.53) and neurodevelopmental risks in pregnancy [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite this compelling evidence, few studies have estimated the impact of specific SNAP policy levers on UPF consumption and downstream health. Fiscal measures\u0026mdash;such as national UPF taxes\u0026mdash;are projected to reduce purchases and improve diet quality in modeling studies [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], while mandatory front-of-pack labeling (FOPL) yields significant sodium and sugar declines in simulation [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. SNAP-specific microsimulations suggest that removing sugar-sweetened beverages (SSBs) could reduce obesity, CVD, and T2D burden [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], yet implementation risks, beneficiary acceptability, and potential compensation through non-SNAP funds complicate policy translation. Indeed, qualitative analyses reveal that SNAP restrictions may stigmatize participants without improving long-term diet quality, whereas incentive programs (e.g., fruit and vegetable vouchers) demonstrate more consistent benefits [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Our study fills a critical gap by quantifying the scale of potential nutrient and risk shifts under graded UPF restriction scenarios, providing evidence to weigh restriction versus incentive strategies in future SNAP reforms.\u003c/p\u003e \u003cp\u003eKey limitations might temper the interpretation of our results. First, UPF classification relied on NOVA coding of 2017\u0026ndash;2018 FNDDS descriptions; though validated approaches yield 80\u0026ndash;90% accuracy [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], cycle-specific crosswalks and manual audits could reduce misclassification. The NOVA framework, while widely used, faces critiques over ambiguous definitions and potential for inconsistent assignments [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Second, the substitution logic\u0026mdash;50% sodium cut, 80% sugar cut, and 50% fiber boost\u0026mdash;reflects optimistic nutrient profiles of minimally processed alternatives; real-world compliance may be lower, and cross-price elasticities could lead households to offset SNAP-restricted UPFs with purchases outside the program [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Third, our Monte Carlo simulations capture within-person variability but do not propagate complex-survey design uncertainty introduced by nutrient recalculations; future analyses should incorporate replicate weights or Taylor linearization to refine confidence intervals.\u003c/p\u003e \u003cp\u003eCost-effectiveness and policy feasibility remain open questions. Early economic models demonstrate that modest dietary interventions can yield favorable cost-utility ratios when accounting for healthcare savings from reduced CVD and T2D burden [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], yet full economic evaluations of SNAP UPF restrictions\u0026mdash;including administrative costs, lost retail sales, and potential welfare impacts\u0026mdash;are lacking. Pilot trials or natural experiments (e.g., state-level SNAP waivers with nutrition incentives) could provide real-world data to complement our simulation. Finally, long-term monitoring of dietary patterns, clinical biomarkers, and program participation rates will be critical to ensure that SNAP reforms achieve intended health goals without unintended adverse effects.\u003c/p\u003e \u003cp\u003eIn conclusion, while complete UPF replacement within SNAP yields modest per-person nutrient and risk reductions, the aggregate public health impact could be substantial when scaled to millions of beneficiaries. Our NHANES-based simulation offers a rigorous, transparent framework for policymakers to estimate the nutritional consequences of restricting UPF purchases. Future work should refine classification methods, incorporate behavioral economics, expand equity-focused subgroup analyses, and evaluate cost-effectiveness to inform balanced SNAP policy reforms that advance both food security and nutritional health.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study used de‑identified data from the publicly available NHANES database and did not involve direct patient contact or the use of individually identifiable health information. Under the U.S. Common Rule, research using only publicly available, de‑identified data is exempt from institutional review board oversight; therefore, ethics approval and patient consent were not required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset analyzed during the current study is available in the NHANES repository: https://www.cdc.gov/nchs/nhanes/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo external funding was received for this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAH conceived the study, performed data extraction and statistical analyses, and drafted the manuscript. PS assisted with critical revision of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAndreyeva T, Tripp AS, Schwartz MB (2015) Dietary Quality of Americans by Supplemental Nutrition Assistance Program Participation Status: A Systematic Review. Am J Prev Med 49(4):594\u0026ndash;604\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMonteiro CA, Cannon G, Levy RB, Moubarac JC, Louzada ML, Rauber F, Khandpur N, Cediel G, Neri D, Martinez-Steele E et al (2019) Ultra-processed foods: what they are and how to identify them. Public Health Nutr 22(5):936\u0026ndash;941\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMonteiro CA, Cannon G, Moubarac JC, Levy RB, Louzada MLC, Jaime PC (2018) The UN Decade of Nutrition, the NOVA food classification and the trouble with ultra-processing. Public Health Nutr 21(1):5\u0026ndash;17\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMonteiro CA, Levy RB, Claro RM, Castro IR, Cannon G (2010) A new classification of foods based on the extent and purpose of their processing. Cad Saude Publica 26(11):2039\u0026ndash;2049\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMonteiro CA, Astrup A (2022) Does the concept of ultra-processed foods help inform dietary guidelines, beyond conventional classification systems? YES. Am J Clin Nutr 116(6):1476\u0026ndash;1481\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGibney MJ (2019) Ultra-Processed Foods: Definitions and Policy Issues. Curr Dev Nutr 3(2):nzy077\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFiolet T, Srour B, Sellem L, Kesse-Guyot E, All\u0026egrave;s B, M\u0026eacute;jean C, Deschasaux M, Fassier P, Latino-Martel P, Beslay M et al (2018) Consumption of ultra-processed foods and cancer risk: results from NutriNet-Sant\u0026eacute; prospective cohort. BMJ 360:k322\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIsaksen IM, Dankel SN (2023) Ultra-processed food consumption and cancer risk: A systematic review and meta-analysis. Clin Nutr 42(6):919\u0026ndash;928\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSrour B, Fezeu LK, Kesse-Guyot E, All\u0026egrave;s B, Debras C, Druesne-Pecollo N, Chazelas E, Deschasaux M, Hercberg S, Galan P et al (2020) Ultraprocessed Food Consumption and Risk of Type 2 Diabetes Among Participants of the NutriNet-Sant\u0026eacute; Prospective Cohort. JAMA Intern Med 180(2):283\u0026ndash;291\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHall KD, Ayuketah A, Brychta R, Cai H, Cassimatis T, Chen KY, Chung ST, Costa E, Courville A, Darcey V et al (2019) Ultra-Processed Diets Cause Excess Calorie Intake and Weight Gain: An Inpatient Randomized Controlled Trial of Ad Libitum Food Intake. Cell Metab 30(1):67\u0026ndash;77e63\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe FJ, MacGregor GA (2011) Salt reduction lowers cardiovascular risk: meta-analysis of outcome trials. Lancet 378(9789):380\u0026ndash;382\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe FJ, MacGregor GA Effect of longer-term modest salt reduction on blood pressure. Cochrane Database Syst Rev 2004(3):Cd004937\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrummon AH, Smith NR, Golden SD, Frerichs L, Taillie LS, Brewer NT (2019) Health Warnings on Sugar-Sweetened Beverages: Simulation of Impacts on Diet and Obesity Among U.S. Adults. Am J Prev Med 57(6):765\u0026ndash;774\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLane MM, Gamage E, Du S, Ashtree DN, McGuinness AJ, Gauci S, Baker P, Lawrence M, Rebholz CM, Srour B et al (2024) Ultra-processed food exposure and adverse health outcomes: umbrella review of epidemiological meta-analyses. BMJ 384:e077310\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePagliai G, Dinu M, Madarena MP, Bonaccio M, Iacoviello L, Sofi F (2021) Consumption of ultra-processed foods and health status: a systematic review and meta-analysis. Br J Nutr 125(3):308\u0026ndash;318\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Z, Lu C, Cui L, Fenfen E, Shang W, Wang Z, Song G, Yang K, Li X (2024) Consumption of ultra-processed foods and multiple health outcomes: An umbrella study of meta-analyses. Food Chem 434:137460\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTalebi S, Mehrabani S, Ghoreishy SM, Wong A, Moghaddam A, Feyli PR, Amirian P, Zarpoosh M, Kermani MAH, Moradi S (2024) The association between ultra-processed food and common pregnancy adverse outcomes: a dose-response systematic review and meta-analysis. BMC Pregnancy Childbirth 24(1):369\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eValizadeh P, Ng SW (2024) Promoting Healthier Purchases: Ultraprocessed Food Taxes and Minimally Processed Foods Subsidies for the Low Income. Am J Prev Med 67(1):3\u0026ndash;14\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFlexner N, Zaltz D, Greenthal E, Musicus AA, Ahmed M, L\u0026rsquo;Abbe MR (2025) Estimating the dietary and health impact of implementing mandatory front-of-package nutrient disclosures in the US: A policy scenario modeling analysis. PLoS ONE 20(2):e0312638\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeung CW, Wolfson JA (2025) A Path Forward for Strengthening SNAP Nutrition Policy\u0026mdash;Making the US Healthy Again. JAMA Health Forum 6(5):e251115\u0026ndash;e251115\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParks CA, Han P, Fricke HE, Parker HA, Hesterman OB, Yaroch AL (2021) Reducing food insecurity and improving fruit and vegetable intake through a nutrition incentive program in Michigan, USA. SSM - Popul Health 15:100898\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSneed NM, Ukwuani S, Sommer EC, Samuels LR, Truesdale KP, Matheson D, Noerper TE, Barkin SL, Heerman WJ (2023) Reliability and validity of assigning ultraprocessed food categories to 24-h dietary recall data. Am J Clin Nutr 117(1):182\u0026ndash;190\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSteele EM, O'Connor LE, Juul F, Khandpur N, Galastri Baraldi L, Monteiro CA, Parekh N, Herrick KA (2023) Identifying and Estimating Ultraprocessed Food Intake in the US NHANES According to the Nova Classification System of Food Processing. J Nutr 153(1):225\u0026ndash;241\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBraesco V, Souchon I, Sauvant P, Haurogn\u0026eacute; T, Maillot M, F\u0026eacute;art C, Darmon N (2022) Ultra-processed foods: how functional is the NOVA system? Eur J Clin Nutr 76(9):1245\u0026ndash;1253\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJung S, Kim JY, Park S, Lee JE (2025) Potential misclassification of ultra-processed foods across studies and the need for a unified classification system: a scoping review. Nutr Res Pract 19(3):331\u0026ndash;344\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeung CW, Fulay AP, Parnarouskis L, Martinez-Steele E, Gearhardt AN, Wolfson JA (2022) Food insecurity and ultra-processed food consumption: the modifying role of participation in the Supplemental Nutrition Assistance Program (SNAP). Am J Clin Nutr 116(1):197\u0026ndash;205\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFulay AP, Baylin A, Wolfson JA, Lee JM, Martinez-Steele E, Leung CW (2025) Associations between Food Insecurity and Supplemental Nutrition Assistance Program (SNAP) participation with ultra-processed food intake in lower-income U.S. adolescents. J Nutritional Sci 14:e41\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD\u0026ouml;tsch-Klerk M, Bruins MJ, Detzel P, Martikainen J, Nergiz-Unal R, Roodenburg AJC, Pekcan AG (2023) Modelling health and economic impact of nutrition interventions: a systematic review. Eur J Clin Nutr 77(4):413\u0026ndash;426\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEmmert-Fees KMF, Karl FM, von Philipsborn P, Rehfuess EA, Laxy M (2021) Simulation Modeling for the Economic Evaluation of Population-Based Dietary Policies: A Systematic Scoping Review. Adv Nutr 12(5):1957\u0026ndash;1995\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Lebanese University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Ultra-processed foods, SNAP, NHANES, policy simulation, dietary modeling, sodium intake, added sugar, dietary fiber, cardiovascular disease, type 2 diabetes, systolic blood pressure","lastPublishedDoi":"10.21203/rs.3.rs-6897045/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6897045/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eUltra-processed foods (UPFs), which now account for over half of caloric intake in the U.S., are consistently linked to increased cardiometabolic risk. Participants in the Supplemental Nutrition Assistance Program (SNAP) consume disproportionately high levels of UPFs, contributing to dietary disparities. Despite this, few policy simulations have quantified the potential health benefits of restricting UPF purchases within SNAP.\u003c/p\u003e\u003cp\u003e\u003cb\u003eObjective\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo estimate the nutritional and cardiometabolic health impacts of restricting UPF purchases in SNAP using nationally representative dietary data and a Monte Carlo policy modeling framework.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe conducted a cross-sectional simulation study using NHANES 2007\u0026ndash;2020 data from adults aged 20\u0026ndash;65. Foods were classified by NOVA criteria, and three scenarios were modeled: isocaloric replacement of 25%, 50%, and 100% of UPFs with minimally processed alternatives. Nutrient shifts (sodium, added sugar, fiber) were estimated for SNAP participants and nonparticipants. Health impacts were simulated by applying meta-analytic effect sizes linking these nutrients to systolic blood pressure (SBP), type 2 diabetes (T2D), and cardiovascular disease (CVD) risk.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eFull UPF replacement (100%) among SNAP participants led to reductions of 257 mg/day sodium, 30.7 g/day added sugar, and a gain of 1.13 g/day fiber. These shifts translated to a 0.64 mmHg SBP reduction, 0.25% relative reduction in T2D risk, and 1.01% relative reduction in CVD risk. Nonparticipants experienced slightly greater improvements.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eRestricting UPF purchases in SNAP could yield meaningful population-level improvements in cardiometabolic health. Though individual risk reductions are modest, large-scale implementation may produce substantial public health benefits and help narrow dietary inequities.\u003c/p\u003e","manuscriptTitle":"Simulated Nutritional and Health Impacts of Restricting Ultra-Processed Food Purchases in the SNAP Program: A NHANES-Based Policy Modeling Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-18 19:35:57","doi":"10.21203/rs.3.rs-6897045/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d77a0ba5-2f67-464b-b205-d3642a9eaaf7","owner":[],"postedDate":"June 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-18T19:35:57+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-18 19:35:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6897045","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6897045","identity":"rs-6897045","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}