Home Care Reimbursement, Long-term Care Utilization, and Health Outcomes
Bibliographic record
Abstract
Long-term care currently comprises almost 10% of national health expenditures and is projected to rise rapidly over coming decades.A key, and relatively poorly understood, element of long-term care is home health care.I use a substantial change in Medicare reimbursement policy, which took the form of tightly binding average per-patient reimbursement caps, to address several questions about the market for home care.I find that the reimbursement change was associated with a large drop in the provision of home care.This drop was concentrated among unhealthy beneficiaries, which is consistent with the incentives for patient selection inherent in the per-patient caps.I find that the decline in home health utilization was not offset by increases in institutional long-term care or other medical care and that there were no associated adverse health consequences.However, approximately one-quarter of the decline in Medicare spending was offset by increases in out-ofpocket expenditures for home health care, with the offset concentrated in higher income populations.Despite the value of home health care implied by the out-of-pocket expenditures, I find that the welfare implications of the reimbursement change were ambiguous.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".