High Out‐of‐Pocket Medical Spending among the Poor and Elderly in Nine Developed Countries
Bibliographic record
Abstract
OBJECTIVE: The design of health insurance, and the role out-of-pocket (OOP) payments play in it, is a key policy issue as rising health costs have encouraged greater cost-sharing measures. This paper compares the percentage of Americans spending large amounts OOP to meet their health needs with percentages in eight other developed countries. By disaggregating by age and income, the paper focuses on the poor and elderly populations within each. DATA SOURCE: The study uses nationally representative household survey data made available through the Luxembourg Income Study. It includes nations with high, medium, and low levels of OOP spending. STUDY DESIGN: Households have high medical spending when their OOP expenditures exceed a threshold share of income. I calculate the share of each nation's population, as well as subpopulations within it, with high OOP expenditures. PRINCIPAL FINDINGS: The United States is not alone in exposing large numbers of citizens to high OOP expenses. In six of the other eight countries, one-quarter or more of low-income citizens devoted at least 5 percent of their income to OOP expenses, and in all but two countries, more than 1 in 10 elderly citizens had high medical expenses. CONCLUSIONS: For some populations in the sample nations, health insurance does not provide adequate financial protection and likely contributes to inequities in health care delivery and 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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".