Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2017 Nov 1;177(11):1642-1649.
doi: 10.1001/jamainternmed.2017.4841.

Supplemental Nutrition Assistance Program (SNAP) Participation and Health Care Expenditures Among Low-Income Adults

Affiliations

Supplemental Nutrition Assistance Program (SNAP) Participation and Health Care Expenditures Among Low-Income Adults

Seth A Berkowitz et al. JAMA Intern Med. .

Abstract

Importance: Food insecurity is associated with high health care expenditures, but the effectiveness of food insecurity interventions on health care costs is unknown.

Objective: To determine whether the Supplemental Nutrition Assistance Program (SNAP), which addresses food insecurity, can reduce health care expenditures.

Design, setting, and participants: This is a retrospective cohort study of 4447 noninstitutionalized adults with income below 200% of the federal poverty threshold who participated in the 2011 National Health Interview Survey (NHIS) and the 2012-2013 Medical Expenditure Panel Survey (MEPS).

Exposures: Self-reported SNAP participation in 2011.

Main outcomes and measures: Total health care expenditures (all paid claims and out-of-pocket costs) in the 2012-2013 period. To test whether SNAP participation was associated with lower subsequent health care expenditures, we used generalized linear modeling (gamma distribution, log link, with survey design information), adjusting for demographics (age, gender, race/ethnicity), socioeconomic factors (income, education, Social Security Disability Insurance disability, urban/rural), census region, health insurance, and self-reported medical conditions. We also conducted sensitivity analyses as a robustness check for these modeling assumptions.

Results: A total of 4447 participants (2567 women and 1880 men) were enrolled in the study, mean (SE) age, 42.7 (0.5) years; 1889 were SNAP participants, and 2558 were not. Compared with other low-income adults, SNAP participants were younger (mean [SE] age, 40.3 [0.6] vs 44.1 [0.7] years), more likely to have public insurance or be uninsured (84.9% vs 67.7%), and more likely to be disabled (24.2% vs 10.6%) (P < .001 for all). In age- and gender-adjusted models, health care expenditures between those who did and did not participate in SNAP were similar (difference, $34; 95% CI, -$1097 to $1165). In fully adjusted models, SNAP was associated with lower estimated annual health care expenditures (-$1409; 95% CI, -$2694 to -$125). Sensitivity analyses were consistent with these results, also indicating that SNAP participation was associated with significantly lower estimated expenditures.

Conclusions and relevance: SNAP enrollment is associated with reduced health care spending among low-income American adults, a finding consistent across several analytic approaches. Encouraging SNAP enrollment among eligible adults may help reduce health care costs in the United States.

PubMed Disclaimer

Conflict of interest statement

Conflict of Interest Disclosures: None reported.

Figures

Figure 1.
Figure 1.. Study Flow Diagram
Figure 2.
Figure 2.. Forest Plot Showing the Differences by Analysis Type in Estimated Mean (95% CI) Health Expenditures for Those Who Did and Did Not Receive Supplemental Nutrition Assistance Program (SNAP) Benefits
Note that the estimands are slightly different across the methods: the fully adjusted regression estimates an effect conditional on the covariates; augmented inverse probability weighting (AIPW) estimates average treatment effect (ie, the effect of enrolling in SNAP for the entire population of adults with income <200% federal poverty level); and near/far instrumental variable analysis estimates local average treatment effect (ie, the effect in the marginal case where the instrument made the difference in receipt of SNAP benefits).
Figure 3.
Figure 3.. Forest Plot Comparing the Mean (95% CI) Differences by Sociodemographic and Clinical Subgroups in Estimated Mean Health Expenditures for Those Who Did and Did Not Receive Supplemental Nutrition Assistance Program (SNAP) Benefits
FPL indicates federal poverty level.

Similar articles

Cited by

References

    1. Agency for Healthcare Research and Quality 2014 National Healthcare Quality & Disparities Report. 2015. https://www.ahrq.gov/research/findings/nhqrdr/nhqdr14/index.html. Accessed January 6, 2017.
    1. Blumenthal D, Chernof B, Fulmer T, Lumpkin J, Selberg J. Caring for high-need, high-cost patients: an urgent priority. N Engl J Med. 2016;375(10):909-911. - PubMed
    1. Chetty R, Stepner M, Abraham S, et al. . The association between income and life expectancy in the United States, 2001-2014. JAMA. 2016;315(16):1750-1766. - PMC - PubMed
    1. Berkowitz SA, Baggett TP, Wexler DJ, Huskey KW, Wee CC. Food insecurity and metabolic control among U.S. adults with diabetes. Diabetes Care. 2013;36(10):3093-3099. - PMC - PubMed
    1. Berkowitz SA, Fabreau GE. Food insecurity: what is the clinician’s role? CMAJ. 2015;187(14):1031-1032. - PMC - PubMed

Publication types