The Health Effects of Medicare for the Near‐Elderly Uninsured
Bibliographic record
Abstract
OBJECTIVE: To determine whether Medicare enrollment at age 65 has an effect on the health trajectory of the near-elderly uninsured. DATA SOURCES: Eight biennial waves (1992-2006) of the Health and Retirement Study, a nationally representative panel survey of noninstitutionalized 51-61 year olds and their spouses. STUDY DESIGN: We use a quasi-experimental approach to compare the health effects of insurance for the near-elderly uninsured with previously insured contemporaneous controls. The primary outcome measure is overall self-reported health status combined with mortality (i.e., excellent to very good, good, fair to poor, dead). RESULTS: The change in the trajectory of overall health status for the previously uninsured that can be attributed to Medicare is small and not statistically significant. For every 100 persons in the previously uninsured group, joining Medicare is associated with 0.6 fewer in excellent or very good health (95 percent CI: -4.8, 3.3), 0.3 more in good health (95 percent CI: -3.8, 4.1), 2.5 fewer in fair or poor health (95 percent CI: -7.4, 2.3), and 2.8 more dead (-4.0, 10.0) by age 73. The health trajectory patterns from physician objective health measures are similarly small and not statistically significant. CONCLUSIONS: Medicare coverage at age 65 for the previously uninsured is not linked to improvements in overall health status.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".