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Figure.  Observed Health Status Patterns Among Eventual Supplemental Security Income (SSI) Recipients Before and After Initial Receipt of SSI Benefits
Observed Health Status Patterns Among Eventual Supplemental Security Income (SSI) Recipients Before and After Initial Receipt of SSI Benefits

Each data point is from a separate sample, but data from each sample were collected in June 2010 (circles). For example, the data for 12 months before SSI entry were collected from a sample of individuals who started receiving SSI in June 2011, and the data for 12 months after SSI entry were collected from a sample of individuals who started receiving SSI in June 2009. For comparison, we estimated the health status for individuals who received SSI benefits in all 12 waves (long-term recipients) and for eligible nonrecipients who never received SSI but were eligible based on disability status and age4 and income and asset data provided in the Survey of Income and Program Participation.5 The solid lines represent the prediction model adjusting for covariates, with dashed lines representing the 95% CIs.

Table.  Timeline of Health Status Measurement to Initial Receipt of SSI
Timeline of Health Status Measurement to Initial Receipt of SSI
1.
Coleman-Jensen  A, Nord  M.  Food insecurity among households with working-age adults with disabilities. Economic Research Report 144. Washington, DC: United States Department of Agriculture; 2013. http://ageconsearch.umn.edu/bitstream/142955/2/err_144.pdf. Accessed February 21, 2019.
2.
Nord  M, Golla  AM.  Does SNAP decrease food insecurity: untangling the self-selection effect. Economic Research Report 85. Washington, DC: United States Department of Agriculture; 2009. https://www.ers.usda.gov/webdocs/publications/46295/10977_err85_1_.pdf?v=0. Accessed February 21, 2019.
3.
McGee  DL, Liao  Y, Cao  G, Cooper  RS.  Self-reported health status and mortality in a multiethnic US cohort.  Am J Epidemiol. 1999;149(1):41-46. doi:10.1093/oxfordjournals.aje.a009725PubMedGoogle ScholarCrossref
4.
Gettens  J, Lei  P, Henry  A. Accounting for geographic variation in DI and SSI participation. Princeton, NJ: Mathematica Center for Studying Disability Policy; 2016. https://www.mathematica-mpr.com/-/media/publications/pdfs/disability/2016/drc-wp-geographic-variation.pdf. Accessed February 21, 2019.
5.
Davies  PS, Huynh  M, Newcomb  C, O’Leary  P, Rupp  K, Sears  J.  Modeling SSI financial eligibility and simulating the effect of policy options.  Soc Secur Bull. 2001-2002;64(2):16-45.PubMedGoogle Scholar
6.
Kaiser Family Foundation. Medicaid & CHIP indicators: Medicaid spending and enrollment by enrollment group. Menlo Park, CA: Kaiser Family Foundation, 2018. https://www.kff.org/state-category/medicaid-chip/medicaid-spending-per-enrollee/. Accessed February 21, 2019.
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    Research Letter
    April 1, 2019

    Association of Health Status With Receipt of Supplemental Security Income Among Individuals With Severe Disabilities and Very Low Income and Assets

    Author Affiliations
    • 1Leonard Davis School of Gerontology, The Leonard D. Schaeffer Center for Health Policy & Economics, University of Southern California, Los Angeles
    • 2Bouvé College of Health Sciences, Northeastern University, Boston, Massachusetts
    • 3Lurie Institute for Disability Policy, Heller School for Social Policy and Management, Brandeis University, Waltham, Massachusetts
    JAMA Intern Med. 2019;179(6):842-843. doi:10.1001/jamainternmed.2018.8609

    Supplemental Security Income (SSI) is a cash-transfer program for individuals with severe disabilities and very low incomes and assets. Material hardships such as food insecurity are particularly problematic to the health status of individuals with disabilities.1 These hardships have been shown to increase sharply immediately before people first receive benefits from social welfare programs and then to decrease moderately after benefits start.2 However, whether these material hardship fluctuations are associated with changes in the health status of SSI recipients is unknown. To investigate this possibility, we examined health patterns among recipients before and after initial SSI receipt.

    Methods

    We analyzed data from the first 12 waves of the 2008 panel of the Survey of Income and Program Participation. Waves occurred every 4 months, starting in September 2008. Self-reported health status3 was recorded during wave 6 in June 2010. Supplemental Security Income receipt was recorded in every wave. Data analysis was performed from August 1, 2018, to October 21, 2018. The institutional review board of Brandeis University, Waltham, Massachusetts, determined that the present study was exempt from review given that all data were deidentified.

    Eleven groups of adults (n = 498) 18 years or older whose SSI receipt began during the study period were identified from these data. The groups were composed of individuals whose continuous SSI receipt each began in one of waves 2 through 12. Comparing SSI initiation dates with when health status was recorded in wave 6 allowed us to map each of these groups onto a timeline ranging from 24 months before initial SSI receipt to 16 months after initial SSI receipt (Table and eAppendix in the Supplement).

    Using this timeline, we estimated an interrupted time series (ITS) model in which the percentage of each group reporting a positive health status (excellent, very good, or good vs fair or poor)3 was the dependent variable. The ITS model assessed the pre-SSI trend in health status, as well as changes in this trend after SSI receipt. Stata, version 15.0 (StataCorp) was used to conduct the ITS analysis. The following sociodemographic characteristics were used as covariates: non-Hispanic white race/ethnicity, female, age, high school degree, and married. However, for parsimony, we only included covariates that improved model fit (sex and age in this case).

    Results

    A total of 498 individuals started receiving SSI during the survey period and were included in the main analysis. For qualitative comparison purposes, we also identified 593 individuals who received SSI throughout the survey period and 3856 individuals who were eligible nonrecipients (eAppendix in the Supplement). Of the 498 study individuals in the main analysis, 229 (46%) were men; the mean (SD) age was 53 (16) years. The Figure provides a summary of our findings. In the ITS model, the number of eventual SSI recipients reporting a positive health status decreased before SSI entry at a rate of 0.9 percentage points per month (95% CI, 0.3-1.5 percentage points per month), or over 10 percentage points per year (95% CI, 3.7-17.6 percentage points per year). After SSI entry, the trend flattened to 0.3 percentage points per month (95% CI, −0.8 to 1.3 percentage points per month), a change of 1.1 percentage points per month (95% CI, 0.1-2.2 percentage points per month) from before to after SSI entry.

    Discussion

    There was a sharp decline in health status among eventual SSI recipients in the year before program entry and a modest improvement and stabilization in health status after entry. The pre-entry health decline was likely not associated with disability onset; SSI applications can take several years to process, and the health status of eventual SSI recipients matched the already-poor health status of eligible nonrecipients with disabilities before declining further just before SSI entry (Figure). The findings suggest that fluctuations in material hardships2 that affect health and are addressed by SSI are more likely to be contributors to the health patterns that we found.

    Limitations regarding self-reported data and ITS models using aggregated data are present, although self-reported health status is associated with health outcomes.3 Also, typical ITS concerns about unobserved events driving patterns over time were alleviated because health status was measured at the same time for all groups.

    Any decline in health status among SSI recipients may have systemic implications because people with disabilities disproportionately affect Medicaid utilization and spending.6 Future investigations should explore whether interim supports for SSI applicants, increased SSI benefit levels, or social services screening and referrals for SSI applicants and recipients may improve health outcomes and yield Medicaid savings.

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    Article Information

    Accepted for Publication: January 1, 2019.

    Published Online: April 1, 2019. doi:10.1001/jamainternmed.2018.8609

    Correction: This article was corrected in July 23, 2019, to add a funder to the Funding/Support and a disclaimer.

    Corresponding Author: Rajan A. Sonik, PhD, JD, MPH, Leonard Davis School of Gerontology, The Leonard D. Schaeffer Center for Health Policy & Economics, University of Southern California, 635 Downey Way, Verna & Peter Dauterive Hall, Rm 512A, Los Angeles, CA 90089-3333 (sonik@usc.edu).

    Author Contributions: Dr Sonik had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

    Concept and design: All authors.

    Acquisition, analysis, or interpretation of data: All authors.

    Drafting of the manuscript: All authors.

    Critical revision of the manuscript for important intellectual content: Sonik, Mitra.

    Statistical analysis: Sonik.

    Obtained funding: Sonik.

    Administrative, technical, or material support: Parish.

    Supervision: Parish, Mitra.

    Conflict of Interest Disclosures: None reported.

    Funding/Support: This work was supported by a Social Security Administration Disability Determination Process Small Grant Program grant (Dr Sonik), a Heller Annual Fund grant (Dr Sonik), and the Agency for Healthcare Research and Quality (R03HS026317).

    Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

    Disclaimer: The content is solely the responsibility of the authors and does not necessarily represent the official views of the Agency for Healthcare Research and Quality.

    References
    1.
    Coleman-Jensen  A, Nord  M.  Food insecurity among households with working-age adults with disabilities. Economic Research Report 144. Washington, DC: United States Department of Agriculture; 2013. http://ageconsearch.umn.edu/bitstream/142955/2/err_144.pdf. Accessed February 21, 2019.
    2.
    Nord  M, Golla  AM.  Does SNAP decrease food insecurity: untangling the self-selection effect. Economic Research Report 85. Washington, DC: United States Department of Agriculture; 2009. https://www.ers.usda.gov/webdocs/publications/46295/10977_err85_1_.pdf?v=0. Accessed February 21, 2019.
    3.
    McGee  DL, Liao  Y, Cao  G, Cooper  RS.  Self-reported health status and mortality in a multiethnic US cohort.  Am J Epidemiol. 1999;149(1):41-46. doi:10.1093/oxfordjournals.aje.a009725PubMedGoogle ScholarCrossref
    4.
    Gettens  J, Lei  P, Henry  A. Accounting for geographic variation in DI and SSI participation. Princeton, NJ: Mathematica Center for Studying Disability Policy; 2016. https://www.mathematica-mpr.com/-/media/publications/pdfs/disability/2016/drc-wp-geographic-variation.pdf. Accessed February 21, 2019.
    5.
    Davies  PS, Huynh  M, Newcomb  C, O’Leary  P, Rupp  K, Sears  J.  Modeling SSI financial eligibility and simulating the effect of policy options.  Soc Secur Bull. 2001-2002;64(2):16-45.PubMedGoogle Scholar
    6.
    Kaiser Family Foundation. Medicaid & CHIP indicators: Medicaid spending and enrollment by enrollment group. Menlo Park, CA: Kaiser Family Foundation, 2018. https://www.kff.org/state-category/medicaid-chip/medicaid-spending-per-enrollee/. Accessed February 21, 2019.
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