Medicare spending, managed care and pre-Medicare insurance coverage and associated risks of mortality, deterioration of self-rated health and mental health after four years of Medicare coverage
Bibliographic record
Abstract
National spending on Medicare keeps growing and managed care is reimbursed differently in the United States. Health returns from Medicare spending are not certain. This study aims to quantify the effects of Medicare spending in the first two years of Medicare coverage, managed care and insurance coverage before Medicare (pre-Medicare) on mortality, mental health and self-rated health status after first four years of Medicare coverage. Individuals, who were interviewed from age 65 to 68 years, without Medicare coverage before age 65 years, were included. Health spending (out-of-pocket, OOP) in the first two years of Medicare coverage, their pre-Medicare characteristics and Medicare managed care were used to predict associated risks of mortality, self-rated health status and mental health (Center for Epidemiologic Studies-Depression, CESD scale). Eligible Medicare enrollees (N = 3,503) in the Health and Retirement Study from 1992 to 2011 were chosen. Total health spending was associated with higher likelihood of worse mental health and self-rated health, but OOP spending was associated with risks of health deterioration (p < .05 for all). More OOP health spending in the first two years of Medicare coverage was associated with slightly higher chance of more mental problems, but the magnitude of this association became smaller over time. Medicare managed care did not seem to be beneficial for mortality, mental health or self-rated health status. Expanding pre-Medicare health coverage (through the Affordable Care Act) may not influence health status after first four years of Medicare coverage. Preventing pre-Medicare health conditions may be the priority.
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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 | 0.001 |
| 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 | 0.001 |
| 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".