Universal Health Insurance and the Effect of Cost Containment on Mortality Rates: Strokes and Heart Attacks in Japan
Bibliographic record
Abstract
For more than four decades, Japan has offered universal health insurance. Despite the demand subsidy entailed, it has kept costs low by regulatorily capping the amounts it pays doctors, particularly for the most modern and sophisticated procedures. Facing subsidized demand but stringently capped prices on complex procedures, Japanese physicians have had little incentive to invest in specialized expertise. Instead, they have invested in small private clinics and hospitals. The resulting proliferation of primitive clinics and hospitals has cut both the number of complex modern medical procedures performed, and the number of hospitals with any substantial experience in those procedures. With a quarter of the heart disease in the United States, Japan performs less than 3 percent as many coronary bypass operations and less than 6 percent as many angioplasties. Of the 855 cities and regions in Japan, 77 percent lack any hospital with substantial experience in the sophisticated modern treatment (defined below) of cerebrovascular disease, and 89 percent lack much experience in angioplasties. In this article, I estimate one of the costs of this regulatorily‐driven lack of expertise. Toward that end, I combine mortality data from 855 cities with information on local hospital expertise and local demographic composition. In the typical city, I find that the addition of one hospital with substantial experience in modern stroke treatment would cut annual stroke mortality by 7 to 16 deaths. The addition of one hospital with substantial experience in angioplasties would cut the annual deaths from heart attacks in the city by over 19.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".