Paying ‘Til it Hurts: High Medical Spending among the Poor and Elderly in Ten Developed Countries
Bibliographic record
Abstract
This paper measures high medical expenses in ten developed countries, both overall and by income and age, providing some of the best evidence to date on the extent of high medical spending across and within countries. Using comparable household-level data on out-of pocket (OOP) medical expenditures made available through the Luxembourg Income Study (LIS), we measure high spending when it exceeds a threshold share of household income. The results show that the U.S. is far from alone in its failure to protect individuals from large medical expenses. In five of the other nine countries, one-quarter or more of poor households devoted at least 5 percent of household income to OOP expenses. The rate of high spending in the US is similar to Japan's, but below that in Russia, Poland, Israel, and Switzerland. The high levels of exposure to large medical expenses in most countries indicates the need to develop robust measures of excessive spending that capture both future risk as well as past burdens.
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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 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".