The Incidence of High Medical Expenses by Health Status in Seven Developed Countries
Bibliographic record
Abstract
Health care policy seeks to ensure that citizens are protected against excessive out-of-pocket (OOP) expenses. Yet rising health care costs are pressuring private and social insurance schemes to shift toward more cost-sharing measures. This paper uses household surveys from seven countries to measure the burden of health expenditures for individuals with similar health conditions. It compares countries based on the extent to which citizens—those with health problems in particular—devote a large share of their income to medical expenses. The paper finds that in all countries but France, and to a lesser extent Slovenia, unhealthy citizens face considerably higher medical costs than do the healthy. As many as one-quarter of less healthy citizens in the U.S., Poland, Russia and Israel have large OOP expenses. The paper finds increased exposure to high medical expenses within countries is also associated with increased disparities between the unhealthy and healthy in the financial burden of OOP costs. The levels of high OOP spending uncovered, and their disparate weight on those with health problems (who are also disproportionately poor and elderly) underscore the potential for high OOP expenses to undermine core objectives of health care systems, including those of equitable financing, equal access, and improved medical outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".