Widow(er) Poverty and Out-of-Pocket Medical Expenditures Near the End of Life
Bibliographic record
Abstract
OBJECTIVES: Elderly widows are three times as likely to live in poverty as older married people. This study investigates the gap in poverty, income, and wealth between these groups. Focus is placed on the role played by out-of-pocket medical expenditures spent on dying spouses. METHODS: A national panel survey of people age 70 and older in 1993 was used. Income, poverty, wealth, and out-of-pocket expenditures were examined before and after widowhood, with comparisons made with couples not experiencing a death. RESULTS: Forty-four percent of the difference in economic status between widow(er)s and married elderly persons was due to disparities in economic status that existed prior to widowhood. The remaining 56% was due to factors more directly related to the death of a spouse, including the loss of income and expenses associated with dying. On average, out-of-pocket medical expenditures in the final 2 years of life were equal to 30% of the couple's annual income. For couples in the bottom quarter of the income distribution, these expenditures were 70% of their income. DISCUSSION: As policy makers continue to debate expansions and reforms of Medicare, the potential effects of these reforms on economic well-being, particularly among widows, should be considered.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".