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Original Research
19 October 2021

Supplemental Nutrition Assistance Program Participation and Health Care Use in Older Adults: A Cohort Study

Authors: Seth A. Berkowitz, MD, MPH https://orcid.org/0000-0003-1030-4297, Deepak Palakshappa, MD, MSHP https://orcid.org/0000-0003-1467-4965, Joseph Rigdon, PhD https://orcid.org/0000-0001-6265-0752, Hilary K. Seligman, MD, MAS, and Sanjay Basu, MD, PhD https://orcid.org/0000-0002-0599-6332Author, Article, & Disclosure Information
Publication: Annals of Internal Medicine
Volume 174, Number 12
https://doi.org/10.7326/M21-1588
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Visual Abstract. Supplemental Nutrition Assistance Program Participation and Health Care Use.
The Supplemental Nutrition Assistance Program (SNAP) provides assistance to reduce food insecurity. The authors examined the effect of SNAP enrollment on health care use for older adults dually enrolled in Medicare and Medicaid in North Carolina.

Abstract

Background:

Older adults dually eligible for Medicare and Medicaid have particularly high food insecurity prevalence and health care use.

Objective:

To determine whether participation in the Supplemental Nutrition Assistance Program (SNAP), which reduces food insecurity, is associated with lower health care use and cost for older adults dually eligible for Medicare and Medicaid.

Design:

An incident user retrospective cohort study design was used. The association between participation in SNAP and health care use and cost using outcome regression was assessed and supplemented by entropy balancing, matching, and instrumental variable analyses.

Setting:

North Carolina, September 2016 through July 2020.

Participants:

Older adults (aged ≥65 years) dually enrolled in Medicare and Medicaid but not initially enrolled in SNAP.

Measurements:

Inpatient admissions (primary outcome), emergency department visits, long-term care admissions, and Medicaid expenditures.

Results:

Of 115 868 persons included, 5093 (4.4%) enrolled in SNAP. Mean follow-up was approximately 22 months. In outcome regression analyses, SNAP enrollment was associated with fewer inpatient hospitalizations (−24.6 [95% CI, −40.6 to −8.7]), emergency department visits (−192.7 [CI, −231.1 to −154.4]), and long-term care admissions (−65.2 [CI, −77.5 to −52.9]) per 1000 person-years as well as fewer dollars in Medicaid payments per person per year (−$2360 [CI, −$2649 to −$2071]). Results were similar in entropy balancing, matching, and instrumental variable analyses.

Limitation:

Single state, no Medicare claims data available, and possible residual confounding.

Conclusion:

Participation in SNAP was associated with fewer inpatient admissions and lower health care costs for older adults dually eligible for Medicare and Medicaid.

Primary Funding Source:

National Institutes of Health.

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References

1.
Coleman-Jensen A, Rabbitt MP, Gregory CA, et al. Household food security in the United States in 2019. Accessed at www.ers.usda.gov/publications/pub-details/?pubid=99281 on 3 November 2020.
Google Scholar
2.
Gundersen C, Hake M, Dewey A, et al. Food insecurity during COVID-19. Appl Econ Perspect Policy. 2020. [PMID: 33042509]  doi: 10.1002/aepp.13100
Crossref
PubMed
Google Scholar
3.
Seligman HK, Berkowitz SA. Aligning programs and policies to support food security and public health goals in the United States. Annu Rev Public Health. 2019;40:319-337. [PMID: 30444684]  doi: 10.1146/annurev-publhealth-040218-044132
Crossref
PubMed
Google Scholar
4.
Leung CW, Epel ES, Ritchie LD, et al. Food insecurity is inversely associated with diet quality of lower-income adults. J Acad Nutr Diet. 2014;114:1943-53.e2. [PMID: 25091796]  doi: 10.1016/j.jand.2014.06.353
Crossref
PubMed
Google Scholar
5.
Orr CJ, Keyserling TC, Ammerman AS, et al. Diet quality trends among adults with diabetes by socioeconomic status in the U.S.: 1999-2014. BMC Endocr Disord. 2019;19:54. [PMID: 31151439]  doi: 10.1186/s12902-019-0382-3
Crossref
PubMed
Google Scholar
6.
Morales ME, Berkowitz SA. The relationship between food insecurity, dietary patterns, and obesity. Curr Nutr Rep. 2016;5:54-60. [PMID: 29955440]  doi: 10.1007/s13668-016-0153-y
Crossref
PubMed
Google Scholar
7.
Arenas DJ, Thomas A, Wang J, et al. A systematic review and meta-analysis of depression, anxiety, and sleep disorders in US adults with food insecurity. J Gen Intern Med. 2019;34:2874-2882. [PMID: 31385212]  doi: 10.1007/s11606-019-05202-4
Crossref
PubMed
Google Scholar
8.
Leung CW, Epel ES, Willett WC, et al. Household food insecurity is positively associated with depression among low-income Supplemental Nutrition Assistance Program participants and income-eligible nonparticipants. J Nutr. 2015;145:622-7. [PMID: 25733480]  doi: 10.3945/jn.114.199414
Crossref
PubMed
Google Scholar
9.
Silverman J, Krieger J, Kiefer M, et al. The relationship between food insecurity and depression, diabetes distress and medication adherence among low-income patients with poorly-controlled diabetes. J Gen Intern Med. 2015;30:1476-80. [PMID: 25917659]  doi: 10.1007/s11606-015-3351-1
Crossref
PubMed
Google Scholar
10.
Berkowitz SA, Seligman HK, Choudhry NK. Treat or eat: food insecurity, cost-related medication underuse, and unmet needs. Am J Med. 2014;127:303-310.e3. [PMID: 24440543]  doi: 10.1016/j.amjmed.2014.01.002
Crossref
PubMed
Google Scholar
11.
Gundersen C, Ziliak JP. Food insecurity and health outcomes. Health Aff (Millwood). 2015;34:1830-9. [PMID: 26526240]  doi: 10.1377/hlthaff.2015.0645
Crossref
PubMed
Google Scholar
12.
Wilder ME, Kulie P, Jensen C, et al. The impact of social determinants of health on medication adherence: a systematic review and meta-analysis. J Gen Intern Med. 2021;36:1359-1370. [PMID: 33515188]  doi: 10.1007/s11606-020-06447-0
Crossref
PubMed
Google Scholar
13.
Tarasuk V, Cheng J, de Oliveira C, et al. Association between household food insecurity and annual health care costs. CMAJ. 2015;187:E429-E436. [PMID: 26261199]  doi: 10.1503/cmaj.150234
Crossref
PubMed
Google Scholar
14.
Garcia SP, Haddix A, Barnett K. Incremental health care costs associated with food insecurity and chronic conditions among older adults. Prev Chronic Dis. 2018;15:E108. [PMID: 30171678]  doi: 10.5888/pcd15.180058
Crossref
PubMed
Google Scholar
15.
Berkowitz SA, Basu S, Meigs JB, et al. Food insecurity and health care expenditures in the United States, 2011-2013. Health Serv Res. 2018;53:1600-1620. [PMID: 28608473]  doi: 10.1111/1475-6773.12730
Crossref
PubMed
Google Scholar
16.
Berkowitz SA, Seligman HK, Meigs JB, et al. Food insecurity, healthcare utilization, and high cost: a longitudinal cohort study. Am J Manag Care. 2018;24:399-404. [PMID: 30222918]
PubMed
Google Scholar
17.
Dean EB, French MT, Mortensen K. Food insecurity, health care utilization, and health care expenditures. Health Serv Res. 2020;55 Suppl 2:883-893. [PMID: 32187388]  doi: 10.1111/1475-6773.13283
Crossref
PubMed
Google Scholar
18.
U.S. Department of Agriculture Food and Nutrition Service. Supplemental Nutrition Assistance Program (SNAP). Accessed at www.fns.usda.gov/snap/supplemental-nutrition-assistance-program on 19 February 2020.
Google Scholar
19.
Ratcliffe C, McKernan S. How much does SNAP reduce food insecurity? Accessed at www.ers.usda.gov/publications/pub-details/?pubid=84335 on 3 July 2020.
Google Scholar
20.
Swann CA. Household history, SNAP participation, and food insecurity. Food Policy. 2017;73:1–9. doi: 10.1016/j.foodpol.2017.08.006
Crossref
Google Scholar
21.
U.S. Department of Agriculture Food and Nutrition Service. Trends in SNAP participation rates: FY 2010-2017. Accessed at www.fns.usda.gov/snap/trends-supplemental-nutrition-assistance-program-participation-rates-fiscal-year-2010 on 1 April 2021.
Google Scholar
22.
Gregory CA, Ploeg MV, Andrews M, et al. Supplemental Nutrition Assistance Program (SNAP) participation leads to modest changes in diet quality. Accessed at www.ers.usda.gov/publications/pub-details/?pubid=45062 on 9 February 2021.
Google Scholar
23.
Hastings J, Kessler R, Shapiro JM. The effect of SNAP on the composition of purchased foods: evidence and implications. Am Econ J Econ Policy. 2021;13:277-315. doi: 10.1257/pol.20190350
Crossref
Google Scholar
24.
Center on Budget and Policy Priorities. Chart book: SNAP helps struggling families put food on the table. Accessed at www.cbpp.org/research/food-assistance/chart-book-snap-helps-struggling-families-put-food-on-the-table on 9 February 2021.
Google Scholar
25.
U.S. Department of Agriculture Food and Nutrition Service. SNAP data tables. Accessed at www.fns.usda.gov/pd/supplemental-nutrition-assistance-program-snap on 9 March 2021.
Google Scholar
26.
Food Research & Action Center. The positive effect of SNAP benefits on participants and communities. Accessed at https://frac.org/programs/supplemental-nutrition-assistance-program-snap/positive-effect-snap-benefits-participants-communities on 9 February 2021.
Google Scholar
27.
Wheaton L, Tran V. The antipoverty effects of the Supplemental Nutrition Assistance Program. Accessed at www.urban.org/research/publication/antipoverty-effects-supplemental-nutrition-assistance-program on 9 February 2021.
Google Scholar
28.
Berkowitz SA, Seligman HK, Rigdon J, et al. Supplemental Nutrition Assistance Program (SNAP) participation and health care expenditures among low-income adults. JAMA Intern Med. 2017;177:1642-1649. [PMID: 28973507]  doi: 10.1001/jamainternmed.2017.4841
Crossref
PubMed
Google Scholar
29.
Samuel LJ, Szanton SL, Cahill R, et al. Does the Supplemental Nutrition Assistance Program affect hospital utilization among older adults? The case of Maryland. Popul Health Manag. 2018;21:88-95. [PMID: 28683219]  doi: 10.1089/pop.2017.0055
Crossref
PubMed
Google Scholar
30.
Szanton SL, Samuel LJ, Cahill R, et al. Food assistance is associated with decreased nursing home admissions for Maryland's dually eligible older adults. BMC Geriatr. 2017;17:162. [PMID: 28738897]  doi: 10.1186/s12877-017-0553-x
Crossref
PubMed
Google Scholar
31.
Sonik RA, Parish SL, Mitra M. Inpatient Medicaid usage and expenditure patterns after changes in Supplemental Nutrition Assistance Program benefit levels. Prev Chronic Dis. 2018;15:E120. [PMID: 30289106]  doi: 10.5888/pcd15.180185
Crossref
PubMed
Google Scholar
32.
Keohane LM, Stevenson DG, Freed S, et al. Trends in Medicare fee-for-service spending growth for dual-eligible beneficiaries, 2007-15. Health Aff (Millwood). 2018;37:1265-1273. [PMID: 30080452]  doi: 10.1377/hlthaff.2018.0143
Crossref
PubMed
Google Scholar
33.
Madden JM, Shetty PS, Zhang F, et al. Risk factors associated with food insecurity in the Medicare population. JAMA Intern Med. 2020;180:144-147. [PMID: 31566656]  doi: 10.1001/jamainternmed.2019.3900
Crossref
PubMed
Google Scholar
34.
Berkowitz SA, Terranova J, Hill C, et al. Meal delivery programs reduce the use of costly health care in dually eligible Medicare and Medicaid beneficiaries. Health Aff (Millwood). 2018;37:535-542. [PMID: 29608345]  doi: 10.1377/hlthaff.2017.0999
Crossref
PubMed
Google Scholar
35.
McKethan A, Berkowitz SA, Cohen M. Focusing on population health at scale—joining policy and technology to improve health. N Engl J Med. 2019;380:113-115. [PMID: 30625065]  doi: 10.1056/NEJMp1809451
Crossref
PubMed
Google Scholar
36.
Center on Budget and Policy Priorities. Opportunities for states to coordinate Medicaid and SNAP renewals. Accessed at www.cbpp.org/research/health/opportunities-for-states-to-coordinate-medicaid-and-snap-renewals on 28 June 2018.
Google Scholar
37.
North Carolina Department of Health and Human Services. Pilot program increase access to public benefits for older dual eligible seniors. Accessed at https://files.nc.gov/ncdhhs/SL-2017-57-Section-11C.8.-b--Dual-Eligibles-2020.pdf on 9 February 2021.
Google Scholar
38.
Finkelstein A, Notowidigdo MJ. Take-up and targeting: experimental evidence from SNAP. Q J Econ. 2019;134:1505–1556. doi: 10.1093/qje/qjz013
Crossref
Google Scholar
39.
Berkowitz SA, Berkowitz TSZ, Meigs JB, et al. Trends in food insecurity for adults with cardiometabolic disease in the United States: 2005-2012. PLoS One. 2017;12:e0179172. [PMID: 28591225]  doi: 10.1371/journal.pone.0179172
Crossref
PubMed
Google Scholar
40.
Seligman HK, Laraia BA, Kushel MB. Food insecurity is associated with chronic disease among low-income NHANES participants. J Nutr. 2010;140:304-10. [PMID: 20032485]  doi: 10.3945/jn.109.112573
Crossref
PubMed
Google Scholar
41.
Crews DC, Kuczmarski MF, Grubbs V, et al; Centers for Disease Control and Prevention Chronic Kidney Disease Surveillance Team. Effect of food insecurity on chronic kidney disease in lower-income Americans. Am J Nephrol. 2014;39:27-35. [PMID: 24434743]  doi: 10.1159/000357595
Crossref
PubMed
Google Scholar
42.
Gagne JJ, Glynn RJ, Avorn J, et al. A combined comorbidity score predicted mortality in elderly patients better than existing scores. J Clin Epidemiol. 2011;64:749-59. [PMID: 21208778]  doi: 10.1016/j.jclinepi.2010.10.004
Crossref
PubMed
Google Scholar
43.
Centers for Medicare & Medicaid Services. List and definition of dual eligibles. Accessed at www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/MedicareEnrpts/downloads/Buy-InDefinitions.pdf on 9 February 2021.
Google Scholar
44.
Dartmouth Atlas Project. The Dartmouth Atlas of Health Care. Accessed at www.dartmouthatlas.org/ on 24 May 2021.
Google Scholar
45.
Begg MD, Parides MK. Separation of individual-level and cluster-level covariate effects in regression analysis of correlated data. Stat Med. 2003;22:2591-602. [PMID: 12898546]
Crossref
PubMed
Google Scholar
46.
Deb P, Norton EC. Modeling health care expenditures and use. Annu Rev Public Health. 2018;39:489-505. [PMID: 29328879]  doi: 10.1146/annurev-publhealth-040617-013517
Crossref
PubMed
Google Scholar
47.
Graubard BI, Korn EL. Predictive margins with survey data. Biometrics. 1999;55:652-9. [PMID: 11318229]
Crossref
PubMed
Google Scholar
48.
Hainmueller J. Entropy balancing for causal effects: a multivariate reweighting method to produce balanced samples in observational studies. Polit Anal. 2012;20:25–46. doi: 10.1093/pan/mpr025
Crossref
Google Scholar
49.
Zhao Q, Percival D. Entropy balancing is doubly robust. J Causal Inference. 2017;5:20160010. doi: 10.1515/jci-2016-0010
Crossref
Google Scholar
50.
Josey KP, Berkowitz SA, Ghosh D, et al. Transporting experimental results with entropy balancing. Stat Med. 2021. [PMID: 34018204]  doi: 10.1002/sim.9031
Crossref
PubMed
Google Scholar
51.
Basu S, Akers M, Berkowitz SA, et al. Comparison of fruit and vegetable intake among urban low-income US adults receiving a produce voucher in 2 cities. JAMA Netw Open. 2021;4:e211757. [PMID: 33749765]  doi: 10.1001/jamanetworkopen.2021.1757
Crossref
PubMed
Google Scholar
52.
Page LC, Lenard MA, Keele L. The design of clustered observational studies in education. AERA Open. 2020;6:2332858420954401. doi: 10.1177/2332858420954401
Crossref
Google Scholar
53.
Ho DE, Imai K, King G, et al. Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference. Polit Anal. 2007;15:199–236. doi: 10.1093/pan/mpl013
Crossref
Google Scholar
54.
Terza JV, Basu A, Rathouz PJ. Two-stage residual inclusion estimation: addressing endogeneity in health econometric modeling. J Health Econ. 2008;27:531-43. [PMID: 18192044]  doi: 10.1016/j.jhealeco.2007.09.009
Crossref
PubMed
Google Scholar
55.
Terza JV. Two-stage residual inclusion estimation in health services research and health economics. Health Serv Res. 2018;53:1890-1899. [PMID: 28568477]  doi: 10.1111/1475-6773.12714
Crossref
PubMed
Google Scholar
56.
Wan F, Small D, Mitra N. A general approach to evaluating the bias of 2-stage instrumental variable estimators. Stat Med. 2018;37:1997-2015. [PMID: 29572890]  doi: 10.1002/sim.7636
Crossref
PubMed
Google Scholar
57.
Rosenbaum PR. Design of Observational Studies. 2nd ed. Springer International Publishing; 2020.
Google Scholar
58.
VanderWeele TJ, Ding P. Sensitivity analysis in observational research: introducing the E-value. Ann Intern Med. 2017;167:268-274. [PMID: 28693043]  doi: 10.7326/M16-2607
Link
Google Scholar
59.
Heflin CM, Ingram SJ, Ziliak JP. The effect of the Supplemental Nutrition Assistance Program on mortality. Health Aff (Millwood). 2019;38:1807-1815. [PMID: 31682512]  doi: 10.1377/hlthaff.2019.00405
Crossref
PubMed
Google Scholar
60.
U.S. Department of Agriculture. Thrifty Food Plan, 2021. August 2021. FNS-916. Accessed at www.fns.usda.gov/resource/thrifty-food-plan-2021 on 15 September 2021.
Google Scholar
61.
Unrath M. Targeting, screening, and retention: evidence from California's food stamps program. Accessed at www.capolicylab.org/wp-content/uploads/2021/02/CalFresh-Working-Paper.pdf on 9 February 2021.
Google Scholar
62.
Venkataramani AS, O’Brien R, Tsai AC. Declining life expectancy in the United States: the need for social policy as health policy. JAMA. 2021;325:621-622. [PMID: 33591352]  doi: 10.1001/jama.2020.26339
Crossref
PubMed
Google Scholar
63.
Lantz PM. “Super-utilizer” interventions: what they reveal about evaluation research, wishful thinking, and health equity. Milbank Q. 2020;98:31-34. [PMID: 32030820]  doi: 10.1111/1468-0009.12449
Crossref
PubMed
Google Scholar

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Information & Authors

Information

Published In

cover image Annals of Internal Medicine
Annals of Internal Medicine
Volume 174 • Number 12 • December 2021
Pages: 1674 - 1682

History

Published online: 19 October 2021
Published in issue: December 2021

Keywords

  1. Elderly
  2. Food
  3. Health care utilization
  4. Health disparities
  5. Health equity
  6. Medicare
  7. Nutrition
  8. Social welfare

Copyright

© 2021 American College of Physicians.

Authors

Affiliations

Seth A. Berkowitz, MD, MPH https://orcid.org/0000-0003-1030-4297
University of North Carolina at Chapel Hill School of Medicine and Cecil G. Sheps Center for Health Services Research, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina (S.A.B.)
View all articles by this author
Deepak Palakshappa, MD, MSHP https://orcid.org/0000-0003-1467-4965
Wake Forest School of Medicine, Winston-Salem, North Carolina (D.P., J.R.)
View all articles by this author
Joseph Rigdon, PhD https://orcid.org/0000-0001-6265-0752
Wake Forest School of Medicine, Winston-Salem, North Carolina (D.P., J.R.)
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Hilary K. Seligman, MD, MAS
University of California San Francisco and Center for Vulnerable Populations at Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, California (H.K.S.)
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Sanjay Basu, MD, PhD https://orcid.org/0000-0002-0599-6332
Center for Primary Care, Harvard Medical School, Boston, Massachusetts, Institute of Health Policy, Management & Evaluation, University of Toronto, Toronto, Ontario, Canada, and School of Public Health, Imperial College London, London, United Kingdom (S.B.).
View all articles by this author
Disclaimer: The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or Centers for Disease Control and Prevention.
Acknowledgment: The authors thank Karin Szymanski, Lisa Dillman, David O’Malley, Matthew Wakeman, and the data team at BDT for providing data and information on BDT's outreach process and program for this study. They were not compensated for their efforts. The authors thank the team at the North Carolina Department of Health and Human Services for providing and linking the data necessary for this project. They were not compensated for their efforts. Finally, the authors thank Lily Wang at the University of North Carolina at Chapel Hill for her assistance in preparing the data files for analysis. She was compensated for her efforts.
Financial Support: By the National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health under award number K23DK109200 (Dr. Berkowitz); the National Heart, Lung, and Blood Institute of the National Institutes of Health under award number K23HL146902 (Dr. Palakshappa); and the Centers for Disease Control and Prevention under cooperative agreement number 5U48DP00498-05 (Dr. Seligman). Medicaid claims data were made available through the Carolina Cost and Quality Initiative, a collaborative partnership between the University of North Carolina at Chapel Hill's Gillings School of Global Public Health and the Cecil G. Sheps Center for Health Services Research.
Reproducible Research Statement: Study protocol: Not available. Statistical code: See the Supplement. Data set: Not available owing to terms of the data use agreement.
Corresponding Author: Seth A. Berkowitz, MD, MPH, University of North Carolina at Chapel Hill, 5034 Old Clinic Building, CB 7110, Chapel Hill, NC 27599; e-mail, [email protected].
Current Author Addresses: Dr. Berkowitz: University of North Carolina at Chapel Hill, 5034 Old Clinic Building, CB 7110, Chapel Hill, NC 27599.
Dr. Palakshappa: Department of Internal Medicine, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157.
Dr. Rigdon: Department of Biostatistics and Data Science, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157.
Dr. Seligman: University of California San Francisco, Box 1339, San Francisco, CA 94143.
Dr. Basu: 635 Huntington Avenue, Second Floor, Boston, MA 02115.
Author Contributions: Conception and design: S.A. Berkowitz, J. Rigdon.
Analysis and interpretation of the data: S.A. Berkowitz, J. Rigdon, H.K. Seligman, S. Basu.
Drafting of the article: S.A. Berkowitz, D. Palakshappa, J. Rigdon.
Critical revision of the article for important intellectual content: D. Palakshappa, J. Rigdon, H.K. Seligman, S. Basu.
Final approval of the article: S.A. Berkowitz, D. Palakshappa, J. Rigdon, H.K. Seligman, S. Basu.
Statistical expertise: S.A. Berkowitz, J. Rigdon, S. Basu.
Obtaining of funding: S.A. Berkowitz.
Administrative, technical, or logistic support: S.A. Berkowitz, J. Rigdon.
Collection and assembly of data: S.A. Berkowitz, J. Rigdon.
This article was published at Annals.org on 19 October 2021.

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Seth A. Berkowitz, Deepak Palakshappa, Joseph Rigdon, et al. Supplemental Nutrition Assistance Program Participation and Health Care Use in Older Adults: A Cohort Study. Ann Intern Med.2021;174:1674-1682. [Epub 19 October 2021]. doi:10.7326/M21-1588

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