Predicting Receipt of Social Security Administration Disability Benefits Using Biomarkers and Other Physiological Measures
Evidence From the Health and Retirement Study
Bibliographic Data
| ID | 12798258 |
|---|---|
| Authors | Laura Blue (0000-0002-1634-2692, Mathematica Policy Research, Washington, DC, USA, corresponding author), Lakhpreet Gill (Mathematica Policy Research, Washington, DC, USA), Jessica Faul (University of Michigan), Jessica D Faul (0000-0002-6969-6478, University of Michigan), Kevin Bradway (Mathematica Policy Research, Washington, DC, USA), David C Stapleton (0000-0001-9147-3046, Mathematica Policy Research), David Stapleton (Mathematica Policy Research, Washington, DC, USA) |
| Year | 2017 |
| Volume | 31 |
| Issue | 4 |
| Pages | 555-579 |
| Publication date | 2017-10-27 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Aging and Health (JOURNAL) |
| Journal identifiers | ISSN: 1552-6887 • E-ISSN: 0898-2643 |
| Publisher | SAGE Publishing (PUBLISHER • US) |
| DOI | 10.1177/0898264317737893 |
| PMID | 29254420 |
| OpenAlex | W2766839666 |
| Language | EN |
| Citations received | 1 |
| References cited | 17 |
Physiological measures have moderate power to predict SSA disability benefit receipt
Actuarial science · Business · Disability benefits · Disability insurance · Economics · Health and Retirement Study · Logistic regression · Propensity score matching · Receipt · Respondent · Social security · Global Health Care Issues · Health disparities and outcomes · Medicine · Psychology · Retirement, Disability, and Employment · Gerontology
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| Unique citing works | 1 |
|---|---|
| Citations per year | 0,2 |
| Citation span | 2021 - 2021 (1) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 1 |