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

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.

Conflict of interest statement

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.

Figures

Fig 1. The CVD-PREDICT microsimulation model.

Transitions were based on a calibrated risk score including age, sex, systolic blood pressure, total cholesterol, HDL-cholesterol, current smoking, and diabetes status. Diabetes outcome was also simulated as a model event. *Baseline risk factors were derived from NHANES 2009–2014, with further annual changes in risk factors incorporating both age and secular trends. MI, myocardial infarction; CHD, coronary heart disease; CVA, cerebrovascular accident; CVD, cardiovascular disease; HDL-cholesterol, high-density lipoprotein cholesterol; NHANES, National Health and Nutrition Examination Survey; RCA, resuscitated cardiac arrest.

Fig 2. Lifetime cost-effectiveness of SNAP F&V incentive, F&V incentive and SSB restriction, and a combined incentive/disincentive program for multiple foods (SNAP-plus), by race/ethnicity and education.

ICERs were calculated as the net change in costs divided by the net change in QALYs, compared to a base scenario of the current policy. Values are shown from a government affordability perspective, considering intervention and food subsidy costs for all SNAP participants including children and adults aged

Fig 3. Lifetime averted total CVD events, diabetes cases, and CVD deaths by age (35–44, 45–54, 55–64, 65–74, 75+) across 3 intervention scenarios (i.e., F&V incentive, F&V incentive/SSB restriction, and SNAP-plus).

CVD, cardiovascular disease; F&V, fruits and vegetables; SSB, sugar-sweetened beverage.

Fig 4. Lifetime averted total CVD cases by age (35–44, 45–54, 55–64, 65–74, 75+), sex (male, female), race/ethnicity (non-Hispanic white, non-Hispanic black, Hispanic, other), insurance status (Medicaid, Medicare, other), and education level (less than high school graduate, high school graduate or some college, college graduate) across 3 intervention scenarios (i.e., F&V incentive, F&V incentive/SSB restriction, and SNAP-plus).

CVD, cardiovascular disease; F&V, fruits and vegetables; HS, high school; NH, non-Hispanic; SSB, sugar-sweetened beverage.

Fig 5. Probabilistic sensitivity analyses for cost-effectiveness of the SNAP F&V incentive, F&V incentive and SSB restriction, and a combined incentive/disincentive program for multiple foods (SNAP-plus), at 5 years and over a lifetime.

Values are presented in cost-effectiveness planes of incremental costs ($ billions) versus incremental quality-adjusted life years (QALYs), compared to a base scenario of usual care. For each scenario, each colored dot depicts 1 of 1,000 Monte Carlo iterations, with the large dot depicting the median incremental cost-effectiveness ratio (ICER, $/QALY); and the ellipse depicting the 95% UIs. Results are presented from the perspective of society (top panels), government affordability including food subsidy costs for SNAP adults only (middle panels), and government affordability including food subsidy costs for all SNAP participants (bottom panels). Negative costs represent cost savings. The diagonal solid black lines represent a value of $150,000/QALY, a recommended threshold for assessing health interventions, with values to the right of the line being cost-effective with an ICER