Distribution of lifetime nursing home use and of out‐of‐pocket spending
Bibliographic record
Abstract
Reliable estimates of the lifetime risk of using a nursing home and the associated out-of-pocket costs are important for the saving decisions by individuals and families, and for the purchase of long-term care insurance. We used data on up to 18 y of nursing home use and out-of-pocket costs drawn from the Health and Retirement Study, a longitudinal household survey representative of the older US population. We accumulated the use and spending by individuals over many years, and we developed and used an individual-level matching method to account for use before and after the observation period. In addition, for forecasting, we estimated a dynamic parametric model of nursing home use and spending. We found that 56% of persons aged 57-61 will stay at least one night in a nursing home during their lifetimes, but only 32% of the cohort will pay anything out of pocket. Averaged over all persons, total out-of-pocket expenditures looking forward from age 57 were approximately $7,300, discounted at 3% per year. However, the 95th percentile of spending was almost $47,000. We conclude that the percentage of people ever staying in nursing homes is substantially higher than previous estimates, at least partly due to an increase in nursing home episodes of short duration. Average lifetime out-of-pocket costs may be affordable, but some people will incur much higher costs.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".