Health insurance and catastrophic illness: a report on the New Cooperative Medical System in rural China
Bibliographic record
Abstract
The overall goal of the paper is to understand the progress of the design and implementation of China's New Cooperative Medical System (NCMS) program between 2004 (the second year of the program) and 2007. In the paper we seek to assess some of the strengths and weaknesses of the program using a panel of national-representative, household survey data that were collected in 2005 and early 2008. According to our data, we confirm the recent reports by the Ministry of Health that there have been substantial improvements to the NCMS program in terms of coverage and participation. We also show that rural individuals also perceive an improvement in service by 2007. While the progress of the NCMS program is clear, there are still weaknesses. Most importantly, the program clearly does not meet one of its key goals of providing insurance against catastrophic illnesses. On average, individuals that required inpatient treatment in 2007 were reimbursed for 15% of their expenditures. Although this is higher than in 2004, on average, as the severity of the illness (in terms of expenditures on health care) rose, the real reimbursement rate (reimbursement amount/total expenditure on medical care) fell. The real reimbursement rate for illnesses that required expenditures between 4000 and 10,000 yuan (over 10,000 yuan) was only 11% (8%). Our analysis shows that one of the limiting factors is constrained funding.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".