Supplemental Nutrition Assistance Program Participation and Health Care Use in Older Adults
Bibliographic record
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".