Simulated nutritional and health impacts of restricting ultra-processed food purchases in the SNAP: A NHANES-based policy modeling study

限制SNAP计划中超加工食品购买的模拟营养和健康影响:一项基于NHANES的政策建模研究

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Abstract

Ultra-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. This study estimates the nutritional and cardiometabolic health impacts of restricting UPF purchases in SNAP using nationally representative dietary data and a Monte Carlo policy modeling framework. We conducted a cross-sectional simulation study using National Health and Nutrition Examination Survey 2007-2020 data from adults aged 18 to 65 years. Foods were classified by NOVA criteria, and 3 scenarios were modeled: isocaloric replacement of 25%, 50%, and 100% of UPFs with minimally processed alternatives. Nutrient shifts (sodium, added sugar, and 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, type 2 diabetes, and cardiovascular disease risk. Full UPF replacement (100%) among SNAP participants led to reductions of 257 mg/d sodium, 30.7 g/d added sugar, and a gain of 1.13 g/d fiber. These shifts translated to a 0.64 mm Hg systolic blood pressure reduction, 0.25% relative reduction in type 2 diabetes risk, and 1.01% relative reduction in cardiovascular disease risk. Nonparticipants experienced slightly greater improvements. 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.

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